}}\preformatted{#> # A labelled, complex, directed network of 1490 nodes and 19090 arcs
+\if{html}{\out{
}}\preformatted{#> # A labelled, complex, directed network of 1490 blogs and 19090 link arcs
#> # A tibble: 1,490 x 3
#> name Leaning Source
#>
@@ -29,11 +29,12 @@
}\if{html}{\out{
}}
}
\usage{
-data(ison_blogs)
+data(irps_blogs)
}
\description{
This network consists of the blogosphere around the time of the 2004
US presidential election until February 2005.
+The 2004 election was the first in which blogging played a significant role.
Ties were constructed from a crawl of the front page of each blog.
Political leaning is indicated as "Liberal" (or left leaning) or
@@ -42,8 +43,9 @@ Some blogs were labelled manually,
based on incoming and outgoing links and posts.
}
\references{
-Adamic, Lada A., and Natalie Glance. 2005.
-"The political blogosphere and the 2004 US Election",
-\emph{Proceedings of the WWW-2005 Workshop on the Weblogging Ecosystem}.
+Adamic, Lada, and Natalie Glance. 2005.
+"The political blogosphere and the 2004 US Election: Divided they blog".
+\emph{LinkKDD '05: Proceedings of the 3rd international workshop on Link discovery}, 36-43.
+\doi{10.1145/1134271.1134277}
}
\keyword{datasets}
diff --git a/man/ison_books.Rd b/man/irps_books.Rd
similarity index 96%
rename from man/ison_books.Rd
rename to man/irps_books.Rd
index 43bead64..53f08943 100644
--- a/man/ison_books.Rd
+++ b/man/irps_books.Rd
@@ -1,8 +1,8 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_ison.R
\docType{data}
-\name{ison_books}
-\alias{ison_books}
+\name{irps_books}
+\alias{irps_books}
\title{One-mode undirected network of co-purchased books about US politics on Amazon}
\format{
\if{html}{\out{
}}\preformatted{#> # A labelled, undirected network of 105 nodes and 441 ties
@@ -29,7 +29,7 @@
}\if{html}{\out{
}}
}
\usage{
-data(ison_books)
+data(irps_books)
}
\description{
This network consists of books about US politics sold by Amazon.com.
diff --git a/man/ison_usstates.Rd b/man/irps_usgeo.Rd
similarity index 86%
rename from man/ison_usstates.Rd
rename to man/irps_usgeo.Rd
index bb42e747..666aee10 100644
--- a/man/ison_usstates.Rd
+++ b/man/irps_usgeo.Rd
@@ -1,11 +1,12 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_ison.R
\docType{data}
-\name{ison_usstates}
-\alias{ison_usstates}
+\name{irps_usgeo}
+\alias{irps_usgeo}
\title{One-mode undirected network of US state contiguity (Meghanathan 2017)}
\format{
-\if{html}{\out{
}}\preformatted{#> # A labelled, undirected network of 50 nodes and 107 ties
+\if{html}{\out{
}}\preformatted{#> # US State Contiguity
+#> # A labelled, undirected network of 50 states and 107 contiguity ties
#> # A tibble: 50 x 3
#> name capitol population
#>
@@ -29,7 +30,7 @@
}\if{html}{\out{
}}
}
\usage{
-data(ison_usstates)
+data(irps_usgeo)
}
\description{
This network is of contiguity between US states.
diff --git a/man/irps_wwi.Rd b/man/irps_wwi.Rd
new file mode 100644
index 00000000..75bd6bd8
--- /dev/null
+++ b/man/irps_wwi.Rd
@@ -0,0 +1,58 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data_ison.R
+\docType{data}
+\name{irps_wwi}
+\alias{irps_wwi}
+\title{One-mode signed network of relationships between European major powers (Antal et al. 2006)}
+\format{
+\if{html}{\out{
}}\preformatted{#> # World War I Protagonists
+#> # A dynamic, labelled, signed, undirected network of 6 European major powers
+#> and 20 relationship ties
+#> # A tibble: 6 x 1
+#> name
+#>
+#> 1 GBR
+#> 2 FRA
+#> 3 RUS
+#> 4 AUH
+#> 5 DEU
+#> 6 ITA
+#> # A tibble: 20 x 5
+#> from to sign begin end
+#>
+#> 1 1 2 -1 1872 1904
+#> 2 1 3 -1 1872 1907
+#> 3 1 4 -1 1872 1918
+#> 4 2 3 -1 1872 1890
+#> 5 2 4 -1 1872 1918
+#> 6 2 5 -1 1872 1918
+#> # i 14 more rows
+}\if{html}{\out{
}}
+}
+\usage{
+data(irps_wwi)
+}
+\description{
+This network records the evolution of the major relationship changes
+between the protagonists of World War I (WWI) from 1872 to 1907.
+It is incomplete both in terms of (eventual) parties to the war as well
+as some other relations, but gives a good overview of the main alliances
+and enmities.
+
+The data series begins with the Three Emperors' League (1872, revived in 1881)
+between Germany, Austria-Hungary, and Russia.
+The Triple Alliance in 1882 joined Germany, Austria-Hungary, and Italy into
+a bloc that lasted until WWI.
+A bilateral alliance between Germany and Russia lapsed in 1890,
+and a French-Russian alliance developed between 1891-1894.
+The Entente Cordiale thawed and then fostered relations between Great Britain
+and France in 1904, and a British-Russian agreement in 1907 bound
+Great Britain, France, and Russia into the Triple Entente.
+}
+\references{
+Antal, Tibor, Pavel Krapivsky, and Sidney Redner. 2006.
+"Social balance on networks: The dynamics of friendship and enmity".
+\emph{Physica D} 224: 130-136.
+\doi{10.1016/j.physd.2006.09.028}
+}
+\keyword{datasets}
diff --git a/man/ison_potter.Rd b/man/ison_potter.Rd
deleted file mode 100644
index e67885de..00000000
--- a/man/ison_potter.Rd
+++ /dev/null
@@ -1,168 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_ison.R
-\docType{data}
-\name{ison_potter}
-\alias{ison_potter}
-\title{Six complex one-mode support data in Harry Potter books (Bossaert and Meidert 2013)}
-\format{
-\if{html}{\out{
}}\preformatted{#> $book1
-#> # A labelled, complex, directed network of 64 nodes and 47 arcs
-#> # A tibble: 64 x 4
-#> name schoolyear gender house
-#>
-#> 1 Adrian Pucey 1989 male Slytherin
-#> 2 Alicia Spinnet 1989 female Gryffindor
-#> 3 Angelina Johnson 1989 female Gryffindor
-#> 4 Anthony Goldstein 1991 male Ravenclaw
-#> 5 Blaise Zabini 1991 male Slytherin
-#> 6 C. Warrington 1989 male Slytherin
-#> # i 58 more rows
-#> # A tibble: 47 x 2
-#> from to
-#>
-#> 1 11 11
-#> 2 11 25
-#> 3 11 26
-#> 4 11 44
-#> 5 11 56
-#> 6 11 58
-#> # i 41 more rows
-#>
-#> $book2
-#> # A labelled, complex, directed network of 64 nodes and 110 arcs
-#> # A tibble: 64 x 4
-#> name schoolyear gender house
-#>
-#> 1 Adrian Pucey 1989 male Slytherin
-#> 2 Alicia Spinnet 1989 female Gryffindor
-#> 3 Angelina Johnson 1989 female Gryffindor
-#> 4 Anthony Goldstein 1991 male Ravenclaw
-#> 5 Blaise Zabini 1991 male Slytherin
-#> 6 C. Warrington 1989 male Slytherin
-#> # i 58 more rows
-#> # A tibble: 110 x 2
-#> from to
-#>
-#> 1 2 2
-#> 2 2 3
-#> 3 2 19
-#> 4 2 20
-#> 5 2 25
-#> 6 2 26
-#> # i 104 more rows
-#>
-#> $book3
-#> # A labelled, complex, directed network of 64 nodes and 104 arcs
-#> # A tibble: 64 x 4
-#> name schoolyear gender house
-#>
-#> 1 Adrian Pucey 1989 male Slytherin
-#> 2 Alicia Spinnet 1989 female Gryffindor
-#> 3 Angelina Johnson 1989 female Gryffindor
-#> 4 Anthony Goldstein 1991 male Ravenclaw
-#> 5 Blaise Zabini 1991 male Slytherin
-#> 6 C. Warrington 1989 male Slytherin
-#> # i 58 more rows
-#> # A tibble: 104 x 2
-#> from to
-#>
-#> 1 2 2
-#> 2 2 3
-#> 3 2 19
-#> 4 2 20
-#> 5 2 25
-#> 6 2 26
-#> # i 98 more rows
-#>
-#> $book4
-#> # A labelled, complex, directed network of 64 nodes and 49 arcs
-#> # A tibble: 64 x 4
-#> name schoolyear gender house
-#>
-#> 1 Adrian Pucey 1989 male Slytherin
-#> 2 Alicia Spinnet 1989 female Gryffindor
-#> 3 Angelina Johnson 1989 female Gryffindor
-#> 4 Anthony Goldstein 1991 male Ravenclaw
-#> 5 Blaise Zabini 1991 male Slytherin
-#> 6 C. Warrington 1989 male Slytherin
-#> # i 58 more rows
-#> # A tibble: 49 x 2
-#> from to
-#>
-#> 1 7 7
-#> 2 7 8
-#> 3 7 25
-#> 4 8 8
-#> 5 8 25
-#> 6 9 9
-#> # i 43 more rows
-#>
-#> $book5
-#> # A labelled, complex, directed network of 64 nodes and 160 arcs
-#> # A tibble: 64 x 4
-#> name schoolyear gender house
-#>
-#> 1 Adrian Pucey 1989 male Slytherin
-#> 2 Alicia Spinnet 1989 female Gryffindor
-#> 3 Angelina Johnson 1989 female Gryffindor
-#> 4 Anthony Goldstein 1991 male Ravenclaw
-#> 5 Blaise Zabini 1991 male Slytherin
-#> 6 C. Warrington 1989 male Slytherin
-#> # i 58 more rows
-#> # A tibble: 160 x 2
-#> from to
-#>
-#> 1 2 2
-#> 2 2 3
-#> 3 2 19
-#> 4 2 20
-#> 5 2 25
-#> 6 2 29
-#> # i 154 more rows
-#>
-#> $book6
-#> # A labelled, complex, directed network of 64 nodes and 74 arcs
-#> # A tibble: 64 x 4
-#> name schoolyear gender house
-#>
-#> 1 Adrian Pucey 1989 male Slytherin
-#> 2 Alicia Spinnet 1989 female Gryffindor
-#> 3 Angelina Johnson 1989 female Gryffindor
-#> 4 Anthony Goldstein 1991 male Ravenclaw
-#> 5 Blaise Zabini 1991 male Slytherin
-#> 6 C. Warrington 1989 male Slytherin
-#> # i 58 more rows
-#> # A tibble: 74 x 2
-#> from to
-#>
-#> 1 11 11
-#> 2 11 25
-#> 3 11 56
-#> 4 11 58
-#> 5 12 12
-#> 6 14 14
-#> # i 68 more rows
-}\if{html}{\out{
}}
-}
-\usage{
-data(ison_potter)
-}
-\description{
-Goele Bossaert and Nadine Meidert coded peer support ties among 64 characters
-in the Harry Potter books.
-Each author coded four of seven books using NVivo,
-with the seventh book coded by both and serving to assess inter-rater reliability.
-The first six books concentrated on adolescent interactions,
-were studied in their paper, and are made available here.
-The peer support ties mean voluntary emotional, instrumental, or informational support,
-or praise from one living, adolescent character to another within the book's pages.
-In addition, nodal attributes name, schoolyear (which doubles as their age),
-gender, and their house assigned by the sorting hat are included.
-}
-\references{
-Bossaert, Goele and Nadine Meidert (2013).
-"'We are only as strong as we are united, as weak as we are divided'. A dynamic analysis of the peer support networks in the Harry Potter books."
-\emph{Open Journal of Applied Sciences}, 3(2): 174-185.
-\doi{10.4236/ojapps.2013.32024}
-}
-\keyword{datasets}
diff --git a/man/ison_thrones.Rd b/man/ison_thrones.Rd
deleted file mode 100644
index f87cea49..00000000
--- a/man/ison_thrones.Rd
+++ /dev/null
@@ -1,45 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_ison.R
-\docType{data}
-\name{ison_thrones}
-\alias{ison_thrones}
-\title{One-mode Game of Thrones kinship (Glander 2017)}
-\format{
-\if{html}{\out{
}}\preformatted{#> # Game of Thrones Kinship
-#> # A labelled, multiplex, directed network of 208 characters and 404 kinship
-#> arcs
-#> # A tibble: 208 x 8
-#> name male culture house popularity house2 color shape
-#>
-#> 1 Alys Arryn 0 House Arryn 0.0803 circ~
-#> 2 Elys Waynwood 0 House Waynwood 0.0702 circ~
-#> 3 Jasper Arryn 1 House Arryn 0.0435 squa~
-#> 4 Jeyne Royce 0 House Royce 0 circ~
-#> 5 Jon Arryn 1 Valemen House Arryn 0.836 squa~
-#> 6 Lysa Arryn 0 House Tully 0 House Tully #F781~ circ~
-#> # i 202 more rows
-#> # A tibble: 404 x 5
-#> from to type color lty
-#> >
-#> 1 6 7 mother #7570B3 solid
-#> 2 3 1 father #1B9E77 solid
-#> 3 3 5 father #1B9E77 solid
-#> 4 5 7 father #1B9E77 solid
-#> 5 10 23 mother #7570B3 solid
-#> 6 10 12 mother #7570B3 solid
-#> # i 398 more rows
-}\if{html}{\out{
}}
-}
-\usage{
-data(ison_thrones)
-}
-\description{
-Shirin Glander extended a data set on character deaths in the TV series Game of Thrones
-with the kinship relationships between the characters, by scraping "A Wiki of Ice and Fire"
-and adding missing information by hand.
-}
-\references{
-Glander, Shirin (2017).
-"\href{https://datascienceplus.com/network-analysis-of-game-of-thrones/}{Network analysis of Game of Thrones}".
-}
-\keyword{datasets}
diff --git a/man/manip_as.Rd b/man/manip_as.Rd
index bb96a3b5..0e393381 100644
--- a/man/manip_as.Rd
+++ b/man/manip_as.Rd
@@ -2,6 +2,7 @@
% Please edit documentation in R/manip_as.R
\name{manip_as}
\alias{manip_as}
+\alias{as_nodelist}
\alias{as_edgelist}
\alias{as_matrix}
\alias{as_igraph}
@@ -13,6 +14,8 @@
\alias{as_diffnet}
\title{Modifying network classes}
\usage{
+as_nodelist(.data)
+
as_edgelist(.data, twomode = FALSE)
as_matrix(.data, twomode = NULL)
@@ -133,12 +136,14 @@ as_network(test)
\seealso{
Other modifications:
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/manip_correlation.Rd b/man/manip_correlation.Rd
index 5219aabd..0f90a160 100644
--- a/man/manip_correlation.Rd
+++ b/man/manip_correlation.Rd
@@ -3,9 +3,12 @@
\name{manip_correlation}
\alias{manip_correlation}
\alias{to_correlation}
+\alias{to_cosine}
\title{Node correlation}
\usage{
to_correlation(.data, method = NULL)
+
+to_cosine(.data)
}
\arguments{
\item{.data}{An object of a manynet-consistent class:
@@ -40,12 +43,14 @@ This function runs in \eqn{O(mn^2)} complexity.
\seealso{
Other modifications:
\code{\link{manip_as}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/manip_deformat.Rd b/man/manip_deformat.Rd
new file mode 100644
index 00000000..3c614a69
--- /dev/null
+++ b/man/manip_deformat.Rd
@@ -0,0 +1,107 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/manip_format.R
+\name{manip_deformat}
+\alias{manip_deformat}
+\alias{to_unnamed}
+\alias{to_undirected}
+\alias{to_unweighted}
+\alias{to_unsigned}
+\alias{to_simplex}
+\alias{to_uniplex}
+\title{Modifying network formats}
+\usage{
+to_unnamed(.data)
+
+to_undirected(.data)
+
+to_unweighted(.data, threshold = 1)
+
+to_unsigned(.data, keep = c("positive", "negative"))
+
+to_simplex(.data)
+
+to_uniplex(.data, tie)
+}
+\arguments{
+\item{.data}{An object of a manynet-consistent class:
+\itemize{
+\item matrix (adjacency or incidence) from \code{{base}} R
+\item edgelist, a data frame from \code{{base}} R or tibble from \code{{tibble}}
+\item igraph, from the \code{{igraph}} package
+\item network, from the \code{{network}} package
+\item tbl_graph, from the \code{{tidygraph}} package
+}}
+
+\item{threshold}{For a matrix, the threshold to binarise/dichotomise at.}
+
+\item{keep}{In the case of a signed network, whether to retain
+the "positive" or "negative" ties.}
+
+\item{tie}{Character string naming a tie attribute to retain from a graph.}
+}
+\value{
+All \code{to_} functions return an object of the same class as that provided.
+So passing it an igraph object will return an igraph object
+and passing it a network object will return a network object,
+with certain modifications as outlined for each function.
+}
+\description{
+These functions reformat manynet-consistent data.
+\itemize{
+\item \code{to_unnamed()} reformats labelled network data to unlabelled network data.
+\item \code{to_undirected()} reformats directed network data to an undirected network,
+so that any pair of nodes with at least one directed edge will be
+connected by an undirected edge in the new network.
+This is equivalent to the "collapse" mode in \code{{igraph}}..
+\item \code{to_unweighted()} reformats weighted network data to unweighted network
+data, with all tie weights removed.
+\item \code{to_unsigned()} reformats signed network data to unsigned network data
+keeping just the "positive" or "negative" ties.
+\item \code{to_simplex()} reformats complex network data, containing loops, to simplex network data, without any loops.
+\item \code{to_uniplex()} reformats multiplex network data to a single type of tie.
+}
+
+If the format condition is not met,
+for example \code{to_undirected()} is used on a network that is already undirected,
+the network data is returned unaltered.
+No warning is given so that these functions can be used to ensure conformance.
+
+Unlike the \verb{as_*()} group of functions,
+these functions always return the same class as they are given,
+only transforming these objects' properties.
+}
+\details{
+Not all functions have methods available for all object classes.
+Below are the currently implemented S3 methods:\tabular{lrrrrr}{
+ \tab data.frame \tab igraph \tab matrix \tab network \tab tbl_graph \cr
+ to_simplex \tab 0 \tab 1 \tab 1 \tab 0 \tab 1 \cr
+ to_undirected \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_uniplex \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_unnamed \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_unsigned \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_unweighted \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+}
+}
+\examples{
+as_tidygraph(create_filled(5)) \%>\%
+ mutate_ties(type = sample(c("friend", "enemy"), 10, replace = TRUE)) \%>\%
+ to_uniplex("friend")
+}
+\seealso{
+Other modifications:
+\code{\link{manip_as}},
+\code{\link{manip_correlation}},
+\code{\link{manip_from}},
+\code{\link{manip_levels}},
+\code{\link{manip_miss}},
+\code{\link{manip_nodes}},
+\code{\link{manip_paths}},
+\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
+\code{\link{manip_project}},
+\code{\link{manip_reformat}},
+\code{\link{manip_scope}},
+\code{\link{manip_split}},
+\code{\link{manip_ties}}
+}
+\concept{modifications}
diff --git a/man/manip_from.Rd b/man/manip_from.Rd
index 04f4252c..25183e70 100644
--- a/man/manip_from.Rd
+++ b/man/manip_from.Rd
@@ -66,11 +66,13 @@ ison_adolescents \%>\%
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/manip_levels.Rd b/man/manip_levels.Rd
index 6c594c0c..bd610122 100644
--- a/man/manip_levels.Rd
+++ b/man/manip_levels.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/manip_reformat.R
+% Please edit documentation in R/manip_format.R
\name{manip_levels}
\alias{manip_levels}
\alias{to_onemode}
@@ -63,11 +63,13 @@ Below are the currently implemented S3 methods:\tabular{lrrrr}{
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/manip_miss.Rd b/man/manip_miss.Rd
index 7c1fd16b..e319ff2d 100644
--- a/man/manip_miss.Rd
+++ b/man/manip_miss.Rd
@@ -53,11 +53,13 @@ Krause, Robert, Mark Huisman, Christian Steglich, and Tom A.B. Snijders. 2020.
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/manip_nodes.Rd b/man/manip_nodes.Rd
index 7283d1a5..1542e15a 100644
--- a/man/manip_nodes.Rd
+++ b/man/manip_nodes.Rd
@@ -105,11 +105,13 @@ Below are the currently implemented S3 methods:\tabular{lrrr}{
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/manip_paths.Rd b/man/manip_paths.Rd
index 8c47be1c..afc49984 100644
--- a/man/manip_paths.Rd
+++ b/man/manip_paths.Rd
@@ -149,11 +149,13 @@ Prim, R.C. 1957.
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/manip_permutation.Rd b/man/manip_permutation.Rd
index ea0e81e9..0578c343 100644
--- a/man/manip_permutation.Rd
+++ b/man/manip_permutation.Rd
@@ -34,11 +34,13 @@ graphr(to_permuted(ison_adolescents), node_size = 4)
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/manip_preformat.Rd b/man/manip_preformat.Rd
new file mode 100644
index 00000000..1be6b03a
--- /dev/null
+++ b/man/manip_preformat.Rd
@@ -0,0 +1,92 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/manip_format.R
+\name{manip_preformat}
+\alias{manip_preformat}
+\alias{to_named}
+\alias{to_signed}
+\alias{to_weighted}
+\title{Modifying network formats}
+\usage{
+to_named(.data, names = NULL)
+
+to_signed(.data, mark = NULL)
+
+to_weighted(.data, measure = NULL)
+}
+\arguments{
+\item{.data}{An object of a manynet-consistent class:
+\itemize{
+\item matrix (adjacency or incidence) from \code{{base}} R
+\item edgelist, a data frame from \code{{base}} R or tibble from \code{{tibble}}
+\item igraph, from the \code{{igraph}} package
+\item network, from the \code{{network}} package
+\item tbl_graph, from the \code{{tidygraph}} package
+}}
+
+\item{names}{Character vector of the node names. NULL by default.}
+
+\item{mark}{A mark (logical vector) the length of the ties in the network.}
+
+\item{measure}{A numeric vector (measure) that will be added as the tie
+weights to the network.
+If this is NULL, then the tie weights will be drawn from a
+Poisson distribution with \eqn{\lambda = 4}.}
+}
+\value{
+All \code{to_} functions return an object of the same class as that provided.
+So passing it an igraph object will return an igraph object
+and passing it a network object will return a network object,
+with certain modifications as outlined for each function.
+}
+\description{
+These functions add some format to manynet-consistent data.
+\itemize{
+\item \code{to_directed()} reformats undirected network data to a directed network.
+\item \code{to_redirected()} reformats the direction of directed network data, flipping any existing direction.
+\item \code{to_reciprocated()} reformats directed network data such that every directed tie is reciprocated.
+\item \code{to_acyclic()} reformats network data to an acyclic graph.
+\item \code{to_named()} reformats unlabelled network data to labelled network data
+from a vector of names or random baby names.
+\item \code{to_signed()} reformats unsigned network data to signed network data
+with signs from a mark vector or at random.
+}
+
+If the format condition is not met,
+for example \code{to_undirected()} is used on a network that is already undirected,
+the network data is returned unaltered.
+No warning is given so that these functions can be used to ensure conformance.
+
+Unlike the \verb{as_*()} group of functions,
+these functions always return the same class as they are given,
+only transforming these objects' properties.
+}
+\details{
+Not all functions have methods available for all object classes.
+Below are the currently implemented S3 methods:\tabular{lrrrrr}{
+ \tab data.frame \tab igraph \tab matrix \tab network \tab tbl_graph \cr
+ to_acyclic \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_directed \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_named \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_reciprocated \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_redirected \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_simplex \tab 0 \tab 1 \tab 1 \tab 0 \tab 1 \cr
+}
+}
+\seealso{
+Other modifications:
+\code{\link{manip_as}},
+\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
+\code{\link{manip_from}},
+\code{\link{manip_levels}},
+\code{\link{manip_miss}},
+\code{\link{manip_nodes}},
+\code{\link{manip_paths}},
+\code{\link{manip_permutation}},
+\code{\link{manip_project}},
+\code{\link{manip_reformat}},
+\code{\link{manip_scope}},
+\code{\link{manip_split}},
+\code{\link{manip_ties}}
+}
+\concept{modifications}
diff --git a/man/manip_project.Rd b/man/manip_project.Rd
index ccba3a71..75462284 100644
--- a/man/manip_project.Rd
+++ b/man/manip_project.Rd
@@ -5,7 +5,6 @@
\alias{to_mode1}
\alias{to_mode2}
\alias{to_ties}
-\alias{to_galois}
\title{Modifying networks projection}
\usage{
to_mode1(.data, similarity = c("count", "jaccard", "rand", "pearson", "yule"))
@@ -13,8 +12,6 @@ to_mode1(.data, similarity = c("count", "jaccard", "rand", "pearson", "yule"))
to_mode2(.data, similarity = c("count", "jaccard", "rand", "pearson", "yule"))
to_ties(.data)
-
-to_galois(.data)
}
\arguments{
\item{.data}{An object of a manynet-consistent class:
@@ -58,7 +55,6 @@ of the first node set's (e.g. rows) joint affiliations to nodes in the second no
\item \code{to_mode2()} projects a two-mode network to a one-mode network
of the second node set's (e.g. columns) joint affiliations to nodes in the first node set (rows).
\item \code{to_ties()} projects a network to one where the ties become nodes and incident nodes become their ties.
-\item \code{to_galois()} projects a network to its Galois derivation.
}
}
\details{
@@ -70,11 +66,6 @@ Below are the currently implemented S3 methods:\tabular{lrrrrr}{
to_ties \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
}
}
-\section{Galois lattices}{
-
-Note that the output from \code{to_galois()} is very busy at the moment.
-}
-
\examples{
to_mode1(ison_southern_women)
to_mode2(ison_southern_women)
@@ -87,12 +78,14 @@ to_ties(ison_adolescents)
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
\code{\link{manip_split}},
diff --git a/man/manip_reformat.Rd b/man/manip_reformat.Rd
index fa6ecde2..464f1716 100644
--- a/man/manip_reformat.Rd
+++ b/man/manip_reformat.Rd
@@ -1,44 +1,23 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/manip_reformat.R
+% Please edit documentation in R/manip_format.R
\name{manip_reformat}
\alias{manip_reformat}
-\alias{to_uniplex}
-\alias{to_undirected}
-\alias{to_directed}
-\alias{to_redirected}
-\alias{to_reciprocated}
\alias{to_acyclic}
-\alias{to_unweighted}
-\alias{to_unsigned}
-\alias{to_unnamed}
-\alias{to_named}
-\alias{to_simplex}
\alias{to_anti}
+\alias{to_redirected}
+\alias{to_reciprocated}
+\alias{to_directed}
\title{Modifying network formats}
\usage{
-to_uniplex(.data, tie)
-
-to_undirected(.data)
+to_acyclic(.data)
-to_directed(.data)
+to_anti(.data)
to_redirected(.data)
to_reciprocated(.data)
-to_acyclic(.data)
-
-to_unweighted(.data, threshold = 1)
-
-to_unsigned(.data, keep = c("positive", "negative"))
-
-to_unnamed(.data)
-
-to_named(.data, names = NULL)
-
-to_simplex(.data)
-
-to_anti(.data)
+to_directed(.data)
}
\arguments{
\item{.data}{An object of a manynet-consistent class:
@@ -49,15 +28,6 @@ to_anti(.data)
\item network, from the \code{{network}} package
\item tbl_graph, from the \code{{tidygraph}} package
}}
-
-\item{tie}{Character string naming a tie attribute to retain from a graph.}
-
-\item{threshold}{For a matrix, the threshold to binarise/dichotomise at.}
-
-\item{keep}{In the case of a signed network, whether to retain
-the "positive" or "negative" ties.}
-
-\item{names}{Character vector of the node names. NULL by default.}
}
\value{
All \code{to_} functions return an object of the same class as that provided.
@@ -68,26 +38,13 @@ with certain modifications as outlined for each function.
\description{
These functions reformat manynet-consistent data.
\itemize{
-\item \code{to_uniplex()} reformats multiplex network data to a single type of tie.
-\item \code{to_undirected()} reformats directed network data to an undirected network.
-\item \code{to_directed()} reformats undirected network data to a directed network.
-\item \code{to_redirected()} reformats the direction of directed network data, flipping any existing direction.
-\item \code{to_reciprocated()} reformats directed network data such that every directed tie is reciprocated.
\item \code{to_acyclic()} reformats network data to an acyclic graph.
-\item \code{to_unweighted()} reformats weighted network data to unweighted network data.
-\item \code{to_unsigned()} reformats signed network data to unsigned network data.
-\item \code{to_unnamed()} reformats labelled network data to unlabelled network data.
-\item \code{to_named()} reformats unlabelled network data to labelled network data.
-\item \code{to_simplex()} reformats complex network data, containing loops, to simplex network data, without any loops.
\item \code{to_anti()} reformats network data into its complement, where only ties \emph{not} present in the original network
are included in the new network.
+\item \code{to_redirected()} reformats the direction of directed network data, flipping any existing direction.
+\item \code{to_reciprocated()} reformats directed network data such that every directed tie is reciprocated.
}
-If the format condition is not met,
-for example \code{to_undirected()} is used on a network that is already undirected,
-the network data is returned unaltered.
-No warning is given so that these functions can be used to ensure conformance.
-
Unlike the \verb{as_*()} group of functions,
these functions always return the same class as they are given,
only transforming these objects' properties.
@@ -97,47 +54,21 @@ Not all functions have methods available for all object classes.
Below are the currently implemented S3 methods:\tabular{lrrrrr}{
\tab data.frame \tab igraph \tab matrix \tab network \tab tbl_graph \cr
to_acyclic \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
- to_directed \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
- to_named \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
+ to_anti \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
to_reciprocated \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
to_redirected \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
- to_simplex \tab 0 \tab 1 \tab 1 \tab 0 \tab 1 \cr
- to_undirected \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
- to_uniplex \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
- to_unnamed \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
- to_unsigned \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
- to_unweighted \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr
}
}
\section{Functions}{
\itemize{
-\item \code{to_undirected()}: Returns an object that has any edge direction removed,
-so that any pair of nodes with at least one directed edge will be
-connected by an undirected edge in the new network.
-This is equivalent to the "collapse" mode in \code{{igraph}}.
-
\item \code{to_redirected()}: Returns an object that has any edge direction transposed,
or flipped, so that senders become receivers and receivers become senders.
This essentially has no effect on undirected networks or reciprocated ties.
\item \code{to_reciprocated()}: Returns an object where all ties are reciprocated.
-\item \code{to_unweighted()}: Returns an object that has all edge weights removed.
-
-\item \code{to_unsigned()}: Returns a network with either just the "positive" ties
-or just the "negative" ties
-
-\item \code{to_unnamed()}: Returns an object with all vertex names removed
-
-\item \code{to_named()}: Returns an object that has random vertex names added
-
-\item \code{to_simplex()}: Returns an object that has all loops or self-ties removed
-
}}
\examples{
-as_tidygraph(create_filled(5)) \%>\%
- mutate_ties(type = sample(c("friend", "enemy"), 10, replace = TRUE)) \%>\%
- to_uniplex("friend")
to_anti(ison_southern_women)
#graphr(to_anti(ison_southern_women))
}
@@ -145,12 +76,14 @@ to_anti(ison_southern_women)
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_scope}},
\code{\link{manip_split}},
diff --git a/man/manip_scope.Rd b/man/manip_scope.Rd
index 626c7f8d..0d6d461d 100644
--- a/man/manip_scope.Rd
+++ b/man/manip_scope.Rd
@@ -97,12 +97,14 @@ ison_adolescents \%>\%
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_split}},
diff --git a/man/manip_split.Rd b/man/manip_split.Rd
index bcdd967d..fd664890 100644
--- a/man/manip_split.Rd
+++ b/man/manip_split.Rd
@@ -108,12 +108,14 @@ ison_adolescents \%>\%
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/manip_ties.Rd b/man/manip_ties.Rd
index 3727947f..531efcc0 100644
--- a/man/manip_ties.Rd
+++ b/man/manip_ties.Rd
@@ -91,12 +91,14 @@ delete_ties(ison_adolescents, "Alice|Sue")
Other modifications:
\code{\link{manip_as}},
\code{\link{manip_correlation}},
+\code{\link{manip_deformat}},
\code{\link{manip_from}},
\code{\link{manip_levels}},
\code{\link{manip_miss}},
\code{\link{manip_nodes}},
\code{\link{manip_paths}},
\code{\link{manip_permutation}},
+\code{\link{manip_preformat}},
\code{\link{manip_project}},
\code{\link{manip_reformat}},
\code{\link{manip_scope}},
diff --git a/man/map_graphr.Rd b/man/map_graphr.Rd
index 6aeb3c3b..474f3d4b 100644
--- a/man/map_graphr.Rd
+++ b/man/map_graphr.Rd
@@ -15,6 +15,7 @@ graphr(
node_group,
edge_color,
edge_size,
+ snap = FALSE,
...,
node_colour,
edge_colour
@@ -86,6 +87,8 @@ it is recommended to calculate all edge-related statistics prior
to using this function.
Edges can also be sized by declaring a numeric size or vector instead.}
+\item{snap}{Logical scalar, whether the layout should be snapped to a grid.}
+
\item{...}{Extra arguments to pass on to the layout algorithm, if necessary.}
}
\value{
@@ -122,7 +125,7 @@ try \code{run_tute("Visualisation")}.
\examples{
graphr(ison_adolescents)
ison_adolescents \%>\%
- mutate(color = rep(c("extrovert", "introvert"), times = 4),
+ mutate(color = rep(c("introvert","extrovert"), times = 4),
size = ifelse(node_is_cutpoint(ison_adolescents), 6, 3)) \%>\%
mutate_ties(ecolor = rep(c("friends", "acquaintances"), times = 5)) \%>\%
graphr(node_color = "color", node_size = "size",
diff --git a/man/map_layout_configuration.Rd b/man/map_layout_configuration.Rd
index c4e1b17e..8f8e0f33 100644
--- a/man/map_layout_configuration.Rd
+++ b/man/map_layout_configuration.Rd
@@ -3,15 +3,24 @@
\name{map_layout_configuration}
\alias{map_layout_configuration}
\alias{layout_tbl_graph_configuration}
+\alias{layout_tbl_graph_dyad}
\alias{layout_tbl_graph_triad}
-\alias{layout_tbl_graph_quad}
+\alias{layout_tbl_graph_tetrad}
+\alias{layout_tbl_graph_pentad}
+\alias{layout_tbl_graph_hexad}
\title{Layout algorithms based on configurational positions}
\usage{
layout_tbl_graph_configuration(.data, circular = FALSE, times = 1000)
+layout_tbl_graph_dyad(.data, circular = FALSE, times = 1000)
+
layout_tbl_graph_triad(.data, circular = FALSE, times = 1000)
-layout_tbl_graph_quad(.data, circular = FALSE, times = 1000)
+layout_tbl_graph_tetrad(.data, circular = FALSE, times = 1000)
+
+layout_tbl_graph_pentad(.data, circular = FALSE, times = 1000)
+
+layout_tbl_graph_hexad(.data, circular = FALSE, times = 1000)
}
\arguments{
\item{.data}{An object of a manynet-consistent class:
@@ -30,7 +39,7 @@ Only possible for some layouts. Defaults to FALSE.}
}
\description{
Configurational layouts locate nodes at symmetric coordinates
-to help illustrate the particular layouts.
+to help illustrate particular configurations.
Currently "triad" and "quad" layouts are available.
The "configuration" layout will choose the appropriate configurational
layout automatically.
diff --git a/man/mark_nodes.Rd b/man/mark_nodes.Rd
index ab598dfc..36e9c5ab 100644
--- a/man/mark_nodes.Rd
+++ b/man/mark_nodes.Rd
@@ -3,14 +3,18 @@
\name{mark_nodes}
\alias{mark_nodes}
\alias{node_is_isolate}
+\alias{node_is_pendant}
\alias{node_is_independent}
\alias{node_is_cutpoint}
\alias{node_is_fold}
\alias{node_is_mentor}
+\alias{node_is_neighbor}
\title{Marking nodes based on structural properties}
\usage{
node_is_isolate(.data)
+node_is_pendant(.data)
+
node_is_independent(.data)
node_is_cutpoint(.data)
@@ -18,6 +22,8 @@ node_is_cutpoint(.data)
node_is_fold(.data)
node_is_mentor(.data, elites = 0.1)
+
+node_is_neighbor(.data, node)
}
\arguments{
\item{.data}{An object of a manynet-consistent class:
@@ -42,6 +48,8 @@ for example because they are an isolate,
a tie to themselves (a loop) will be created instead.
Note that this is a different default behaviour than that
described in Valente and Davis (1999).}
+
+\item{node}{Name or index of node.}
}
\description{
These functions return logical vectors the length of the
diff --git a/man/mark_triangles.Rd b/man/mark_triangles.Rd
index f108b042..aeb301cb 100644
--- a/man/mark_triangles.Rd
+++ b/man/mark_triangles.Rd
@@ -8,6 +8,7 @@
\alias{tie_is_cyclical}
\alias{tie_is_simmelian}
\alias{tie_is_forbidden}
+\alias{tie_is_imbalanced}
\title{Marking ties based on structural properties}
\usage{
tie_is_triangular(.data)
@@ -21,6 +22,8 @@ tie_is_cyclical(.data)
tie_is_simmelian(.data)
tie_is_forbidden(.data)
+
+tie_is_imbalanced(.data)
}
\arguments{
\item{.data}{An object of a manynet-consistent class:
@@ -66,6 +69,7 @@ ison_monks \%>\% to_uniplex("like") \%>\%
generate_random(8, directed = TRUE) \%>\%
mutate_ties(forbid = tie_is_forbidden()) \%>\%
graphr(edge_color = "forbid")
+tie_is_imbalanced(ison_marvel_relationships)
}
\seealso{
Other marks:
diff --git a/man/measure_attributes.Rd b/man/measure_attributes.Rd
index 2b731318..7ae9f694 100644
--- a/man/measure_attributes.Rd
+++ b/man/measure_attributes.Rd
@@ -50,7 +50,7 @@ These functions are also often used as helpers within other functions.
as the number of nodes or ties in the network, respectively.
}
\examples{
-node_attribute(ison_lotr, "Race")
+node_attribute(fict_lotr, "Race")
node_names(ison_southern_women)
node_is_mode(ison_southern_women)
tie_attribute(ison_algebra, "task_tie")
diff --git a/man/measure_central_close.Rd b/man/measure_central_close.Rd
index 8c1af71e..ac2a6e3a 100644
--- a/man/measure_central_close.Rd
+++ b/man/measure_central_close.Rd
@@ -8,6 +8,7 @@
\alias{node_information}
\alias{node_eccentricity}
\alias{node_distance}
+\alias{node_vitality}
\alias{tie_closeness}
\alias{net_closeness}
\alias{net_reach}
@@ -26,6 +27,8 @@ node_eccentricity(.data, normalized = TRUE)
node_distance(.data, from, to, normalized = TRUE)
+node_vitality(.data, normalized = TRUE)
+
tie_closeness(.data, normalized = TRUE)
net_closeness(.data, normalized = TRUE, direction = c("all", "out", "in"))
@@ -161,6 +164,14 @@ where the distance from \eqn{i} to \eqn{j} is \eqn{\infty} if unconnected.
As such it is only well defined for connected networks.
}
+\section{Closeness vitality centrality}{
+
+The closeness vitality of a node is the change in the sum of all distances
+in a network, also known as the Wiener Index, when that node is removed.
+Note that the closeness vitality may be negative infinity if
+removing that node would disconnect the network.
+}
+
\examples{
node_closeness(ison_southern_women)
node_reach(ison_adolescents)
@@ -223,6 +234,13 @@ Hage, Per, and Frank Harary. 1995.
\emph{Social Networks}, 17(1): 57-63.
\doi{10.1016/0378-8733(94)00248-9}
}
+
+Koschuetzki, Dirk, Katharina Lehmann, Leon Peeters, Stefan Richter,
+Dagmar Tenfelde-Podehl, and Oliver Zlotowski. 2005.
+"Centrality Indices", in
+Brandes, Ulrik, and Thomas Erlebach (eds.).
+\emph{Network Analysis: Methodological Foundations}.
+Springer: Berlin, pp. 16-61.
}
\seealso{
Other centrality:
diff --git a/man/measure_central_eigen.Rd b/man/measure_central_eigen.Rd
index 31e836dc..a78cc7b3 100644
--- a/man/measure_central_eigen.Rd
+++ b/man/measure_central_eigen.Rd
@@ -12,7 +12,7 @@
\alias{net_eigenvector}
\title{Measures of eigenvector-like centrality and centralisation}
\usage{
-node_eigenvector(.data, normalized = TRUE, scale = FALSE)
+node_eigenvector(.data, normalized = TRUE, scale = TRUE)
node_power(.data, normalized = TRUE, scale = FALSE, exponent = 1)
@@ -100,6 +100,8 @@ where \eqn{a_{i,j} = 1} if \eqn{i} is linked to \eqn{j} and 0 otherwise,
and \eqn{\lambda} is a constant representing the principal eigenvalue.
Rather than performing this iteration,
most routines solve the eigenvector equation \eqn{Ax = \lambda x}.
+Note that since \code{{igraph}} v2.1.1,
+the values will always be rescaled so that the maximum is 1.
}
\section{Power or beta (or Bonacich) centrality}{
diff --git a/man/measure_cohesion.Rd b/man/measure_cohesion.Rd
index 4419f406..8f670e5e 100644
--- a/man/measure_cohesion.Rd
+++ b/man/measure_cohesion.Rd
@@ -62,8 +62,8 @@ please use \code{manynet::to_undirected()} first.
\examples{
net_density(ison_adolescents)
net_density(ison_southern_women)
- net_components(ison_friends)
- net_components(to_undirected(ison_friends))
+ net_components(fict_thrones)
+ net_components(to_undirected(fict_thrones))
net_cohesion(ison_marvel_relationships)
net_cohesion(to_giant(ison_marvel_relationships))
net_adhesion(ison_marvel_relationships)
diff --git a/man/measure_features.Rd b/man/measure_features.Rd
index cc57d496..f119f997 100644
--- a/man/measure_features.Rd
+++ b/man/measure_features.Rd
@@ -44,7 +44,9 @@ This can be created by, among other things, any \verb{node_is_*()} function.}
\item{membership}{A vector of partition membership.}
\item{resolution}{A proportion indicating the resolution scale.
-By default 1.}
+By default 1, which returns the original definition of modularity.
+The higher this parameter, the more smaller communities will be privileged.
+The lower this parameter, the fewer larger communities are likely to be found.}
\item{method}{There are three small-world measures implemented:
\itemize{
@@ -115,6 +117,8 @@ particularly with large networks or depending on the degree of interconnectednes
can miss small clusters that 'hide' inside larger clusters.
In the extreme case, this can be where they are only connected
to the rest of the network through a single tie.
+To help manage this problem, a \code{resolution} parameter is added.
+Please see the argument definition for more details.
}
\examples{
diff --git a/man/measure_properties.Rd b/man/measure_properties.Rd
index 18f2e109..519a2eab 100644
--- a/man/measure_properties.Rd
+++ b/man/measure_properties.Rd
@@ -56,7 +56,7 @@ net_nodes(ison_southern_women)
net_ties(ison_southern_women)
net_dims(ison_southern_women)
net_dims(to_mode1(ison_southern_women))
- net_node_attributes(ison_lotr)
+ net_node_attributes(fict_lotr)
net_tie_attributes(ison_algebra)
}
\seealso{
diff --git a/man/member_community_non.Rd b/man/member_community_non.Rd
index 1c29826e..869ffa1e 100644
--- a/man/member_community_non.Rd
+++ b/man/member_community_non.Rd
@@ -2,6 +2,7 @@
% Please edit documentation in R/member_community.R
\name{member_community_non}
\alias{member_community_non}
+\alias{node_in_community}
\alias{node_in_optimal}
\alias{node_in_partition}
\alias{node_in_infomap}
@@ -11,6 +12,8 @@
\alias{node_in_leiden}
\title{Non-hierarchical community partitioning algorithms}
\usage{
+node_in_community(.data)
+
node_in_optimal(.data)
node_in_partition(.data)
@@ -50,6 +53,9 @@ and larger values make missing ties more important.}
These functions offer algorithms for partitioning
networks into sets of communities:
\itemize{
+\item \code{node_in_community()} runs either optimal or, for larger networks,
+finds the algorithm that maximises modularity and returns that membership
+vector.
\item \code{node_in_optimal()} is a problem-solving algorithm that seeks to maximise
modularity over all possible partitions.
\item \code{node_in_partition()} is a greedy, iterative, deterministic
@@ -69,6 +75,16 @@ The different algorithms offer various advantages in terms of computation time,
availability on different types of networks, ability to maximise modularity,
and their logic or domain of inspiration.
}
+\section{Community}{
+
+This function runs through all available community detection algorithms
+for a given network type, finds the algorithm that returns the
+largest modularity score, and returns the corresponding membership
+partition.
+Where feasible (a small enough network), the optimal problem solving
+technique is used to ensure the maximal modularity partition.
+}
+
\section{Optimal}{
The general idea is to calculate the modularity of all possible partitions,
@@ -130,6 +146,8 @@ where \emph{m} is the total tie weight,
\eqn{n_i} is the node weight of node \emph{i},
and \eqn{\delta(\sigma_i, \sigma_j) = 1} if and only if
\emph{i} and \emph{j} are in the same communities and 0 otherwise.
+Compared to the Louvain method, the Leiden algorithm additionally
+tries to avoid unconnected communities.
}
\examples{
diff --git a/man/member_components.Rd b/man/member_components.Rd
index ebec88b7..63ba59b6 100644
--- a/man/member_components.Rd
+++ b/man/member_components.Rd
@@ -52,11 +52,6 @@ As described in \code{igraph::coreness},
a node's coreness is \emph{k} if it belongs to the \emph{k}-core
but not to the (\emph{k}+1)-core.
}
-\examples{
-ison_monks \%>\% to_uniplex("esteem") \%>\%
- mutate_nodes(comp = node_in_component()) \%>\%
- graphr(node_color = "comp")
-}
\seealso{
Other memberships:
\code{\link{mark_core}},
diff --git a/man/member_equivalence.Rd b/man/member_equivalence.Rd
index db7f3847..efc6af46 100644
--- a/man/member_equivalence.Rd
+++ b/man/member_equivalence.Rd
@@ -15,7 +15,7 @@ node_in_equivalence(
.data,
census,
k = c("silhouette", "elbow", "strict"),
- cluster = c("hierarchical", "concor"),
+ cluster = c("hierarchical", "concor", "cosine"),
distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
range = 8L
)
@@ -23,7 +23,7 @@ node_in_equivalence(
node_in_structural(
.data,
k = c("silhouette", "elbow", "strict"),
- cluster = c("hierarchical", "concor"),
+ cluster = c("hierarchical", "concor", "cosine"),
distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
range = 8L
)
@@ -31,7 +31,7 @@ node_in_structural(
node_in_regular(
.data,
k = c("silhouette", "elbow", "strict"),
- cluster = c("hierarchical", "concor"),
+ cluster = c("hierarchical", "concor", "cosine"),
distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
range = 8L
)
@@ -39,7 +39,7 @@ node_in_regular(
node_in_automorphic(
.data,
k = c("silhouette", "elbow", "strict"),
- cluster = c("hierarchical", "concor"),
+ cluster = c("hierarchical", "concor", "cosine"),
distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
range = 8L
)
@@ -121,12 +121,12 @@ plot(nre)
if(require("sna", quietly = TRUE)){
(nae <- node_in_automorphic(ison_southern_women,
k = "elbow"))
-}
if(require("ggdendro", quietly = TRUE)){
plot(nae)
}
}
}
+}
\seealso{
Other memberships:
\code{\link{mark_core}},
diff --git a/man/model_cluster.Rd b/man/model_cluster.Rd
index 9689b2ce..1f9efc62 100644
--- a/man/model_cluster.Rd
+++ b/man/model_cluster.Rd
@@ -3,11 +3,14 @@
\name{model_cluster}
\alias{model_cluster}
\alias{cluster_hierarchical}
+\alias{cluster_cosine}
\alias{cluster_concor}
\title{Methods for equivalence clustering}
\usage{
cluster_hierarchical(census, distance)
+cluster_cosine(census, distance)
+
cluster_concor(.data, census)
}
\arguments{
@@ -43,7 +46,6 @@ They are exported and listed here to provide more detailed documentation.
}
\section{CONCOR}{
-
First a matrix of Pearson correlation coefficients between each pair of nodes
profiles in the given census is created.
Then, again, we find the correlations of this square, symmetric matrix,
diff --git a/man/motif_net.Rd b/man/motif_net.Rd
index aa92a207..65da8000 100644
--- a/man/motif_net.Rd
+++ b/man/motif_net.Rd
@@ -4,7 +4,7 @@
\alias{motif_net}
\alias{net_by_dyad}
\alias{net_by_triad}
-\alias{net_by_quad}
+\alias{net_by_tetrad}
\alias{net_by_mixed}
\title{Motifs at the network level}
\source{
@@ -15,7 +15,7 @@ net_by_dyad(.data)
net_by_triad(.data)
-net_by_quad(.data)
+net_by_tetrad(.data)
net_by_mixed(.data, object2)
}
@@ -32,19 +32,49 @@ net_by_mixed(.data, object2)
\item{object2}{A second, two-mode migraph-consistent object.}
}
\description{
-These functions include ways to take a census of the positions of nodes
+These functions include ways to take a census of the graphlets
in a network:
\itemize{
\item \code{net_by_dyad()} returns a census of dyad motifs in a network.
\item \code{net_by_triad()} returns a census of triad motifs in a network.
+\item \code{net_by_tetrad()} returns a census of tetrad motifs in a network.
\item \code{net_by_mixed()} returns a census of triad motifs that span
a one-mode and a two-mode network.
}
+
+See also \href{https://www.graphclasses.org/smallgraphs.html}{graph classes}.
+}
+\section{Tetrad census}{
+
+The tetrad census counts the number of four-node configurations in the network.
+The function returns a matrix with a special naming convention:
+\itemize{
+\item E4 (aka co-K4): This is an empty set of four nodes; no ties
+\item I4 (aka co-diamond): This is a set of four nodes with just one tie
+\item H4 (aka co-C4): This set of four nodes includes two non-adjacent ties
+\item L4 (aka co-paw): This set of four nodes includes two adjacent ties
+\item D4 (aka co-claw): This set of four nodes includes three adjacent ties,
+in the form of a triangle with one isolate
+\item U4 (aka P4, four-actor line): This set of four nodes includes three ties
+arranged in a line
+\item Y4 (aka claw): This set of four nodes includes three ties all adjacent
+to a single node
+\item P4 (aka paw, kite): This set of four nodes includes four ties arranged
+as a triangle with an extra tie hanging off of one of the nodes
+\item C4 (aka bifan): This is a symmetric box or 4-cycle or set of shared choices
+\item Z4 (aka diamond): This resembles C4 but with an extra tie cutting across the box
+\item X4 (aka K4): This resembles C4 but with two extra ties cutting across the box;
+a realisation of all possible ties
+}
+
+Graphs of these motifs can be shown using
+\code{plot(net_by_tetrad(ison_southern_women))}.
}
+
\examples{
net_by_dyad(manynet::ison_algebra)
net_by_triad(manynet::ison_adolescents)
-net_by_quad(ison_southern_women)
+net_by_tetrad(ison_southern_women)
marvel_friends <- to_unsigned(ison_marvel_relationships, "positive")
(mixed_cen <- net_by_mixed(marvel_friends, ison_marvel_teams))
}
@@ -67,6 +97,19 @@ Davis, James A., and Samuel Leinhardt. 1967.
“\href{https://files.eric.ed.gov/fulltext/ED024086.pdf}{The Structure of Positive Interpersonal Relations in Small Groups}.” 55.
}
+\subsection{On the tetrad census}{
+
+Ortmann, Mark, and Ulrik Brandes. 2017.
+“Efficient Orbit-Aware Triad and Quad Census in Directed and Undirected Graphs.”
+\emph{Applied Network Science} 2(1):13.
+\doi{10.1007/s41109-017-0027-2}.
+
+McMillan, Cassie, and Diane Felmlee. 2020.
+"Beyond Dyads and Triads: A Comparison of Tetrads in Twenty Social Networks".
+\emph{Social Psychology Quarterly} 83(4): 383-404.
+\doi{10.1177/0190272520944151}
+}
+
\subsection{On the mixed census}{
Hollway, James, Alessandro Lomi, Francesca Pallotti, and Christoph Stadtfeld. 2017.
diff --git a/man/motif_node.Rd b/man/motif_node.Rd
index 5eddc5f9..ee47136a 100644
--- a/man/motif_node.Rd
+++ b/man/motif_node.Rd
@@ -5,7 +5,7 @@
\alias{node_by_tie}
\alias{node_by_dyad}
\alias{node_by_triad}
-\alias{node_by_quad}
+\alias{node_by_tetrad}
\alias{node_by_path}
\title{Motifs at the nodal level}
\usage{
@@ -15,7 +15,7 @@ node_by_dyad(.data)
node_by_triad(.data)
-node_by_quad(.data)
+node_by_tetrad(.data)
node_by_path(.data)
}
@@ -38,18 +38,46 @@ For directed networks, out-ties and in-ties are bound together.
For multiplex networks, the various types of ties are bound together.
\item \code{node_by_triad()} returns a census of the triad configurations
nodes are embedded in.
-\item \code{node_by_quad()} returns a census of nodes' positions
+\item \code{node_by_tetrad()} returns a census of nodes' positions
in motifs of four nodes.
\item \code{node_by_path()} returns the shortest path lengths
of each node to every other node in the network.
}
}
+\section{Tetrad census}{
+
+The nodal tetrad census counts the number of four-node configurations
+that each node is embedded in.
+The function returns a matrix with a special naming convention:
+\itemize{
+\item E4 (aka co-K4): This is an empty set of four nodes; no ties
+\item I4 (aka co-diamond): This is a set of four nodes with just one tie
+\item H4 (aka co-C4): This set of four nodes includes two non-adjacent ties
+\item L4 (aka co-paw): This set of four nodes includes two adjacent ties
+\item D4 (aka co-claw): This set of four nodes includes three adjacent ties,
+in the form of a triangle with one isolate
+\item U4 (aka P4, four-actor line): This set of four nodes includes three ties
+arranged in a line
+\item Y4 (aka claw): This set of four nodes includes three ties all adjacent
+to a single node
+\item P4 (aka paw, kite): This set of four nodes includes four ties arranged
+as a triangle with an extra tie hanging off of one of the nodes
+\item C4 (aka bifan): This is a symmetric box or 4-cycle or set of shared choices
+\item Z4 (aka diamond): This resembles C4 but with an extra tie cutting across the box
+\item X4 (aka K4): This resembles C4 but with two extra ties cutting across the box;
+a realisation of all possible ties
+}
+
+Graphs of these motifs can be shown using
+\code{plot(node_by_tetrad(ison_southern_women))}.
+}
+
\examples{
task_eg <- to_named(to_uniplex(ison_algebra, "tasks"))
(tie_cen <- node_by_tie(task_eg))
node_by_dyad(ison_networkers)
(triad_cen <- node_by_triad(task_eg))
-node_by_quad(ison_southern_women)
+node_by_tetrad(ison_southern_women)
node_by_path(ison_adolescents)
node_by_path(ison_southern_women)
}
@@ -68,6 +96,19 @@ Davis, James A., and Samuel Leinhardt. 1967.
“\href{https://files.eric.ed.gov/fulltext/ED024086.pdf}{The Structure of Positive Interpersonal Relations in Small Groups}.” 55.
}
+\subsection{On the tetrad census}{
+
+Ortmann, Mark, and Ulrik Brandes. 2017.
+“Efficient Orbit-Aware Triad and Quad Census in Directed and Undirected Graphs.”
+\emph{Applied Network Science} 2(1):13.
+\doi{10.1007/s41109-017-0027-2}.
+
+McMillan, Cassie, and Diane Felmlee. 2020.
+"Beyond Dyads and Triads: A Comparison of Tetrads in Twenty Social Networks".
+\emph{Social Psychology Quarterly} 83(4): 383-404.
+\doi{10.1177/0190272520944151}
+}
+
\subsection{On paths}{
Dijkstra, Edsger W. 1959.
diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml
index 55b0b17b..fadfa679 100644
--- a/pkgdown/_pkgdown.yml
+++ b/pkgdown/_pkgdown.yml
@@ -90,7 +90,9 @@ reference:
desc: |
Functions for reformatting networks, retaining the same network dimensions.
contents:
+ - manip_preformat
- manip_reformat
+ - manip_deformat
- subtitle: "Transforming"
desc: |
Functions for transforming networks, which may change the network's dimensions.
@@ -227,13 +229,22 @@ reference:
providing an overview of their properties.
contents:
- ends_with("_tute")
+ - glossary
- subtitle: "Data"
desc: |
The package contains a variety of networks useful for pedagogical purposes
and used in the course 'Social Networks Theories and Methods' and other workshops.
+ There are three collections of data: `ison_` include classic datasets used
+ for introducing network analysis,
+ `fict_` include some popular fictional datasets,
+ and `irps_` include some network datasets that demonstrate the application
+ of social network analysis to International Relations or Political Science
+ topics.
Each page documents the source of the data and its format.
References are provided for further reading and citation.
contents:
- data_overview
- starts_with("ison_")
+ - starts_with("fict_")
+ - starts_with("irps_")
diff --git a/tests/testthat/helper-manynet.R b/tests/testthat/helper-manynet.R
index 82a391d3..09beca30 100644
--- a/tests/testthat/helper-manynet.R
+++ b/tests/testthat/helper-manynet.R
@@ -4,6 +4,23 @@ collect_functions <- function(pattern, package = "manynet"){
getNamespaceExports(package)[grepl(pattern, getNamespaceExports(package))]
}
+expect_values <- function(object, ref) {
+ # 1. Capture object and label
+ # act <- quasi_label(rlang::enquo(object), arg = "object")
+ act <- list(val = object, label = deparse(substitute(object)))
+
+ # 2. Call expect()
+ act$n <- unname(unlist(act$val))
+ ref <- unname(unlist(ref))
+ expect(
+ act$n == ref,
+ sprintf("%s has values %i, not values %i.", act$lab, act$n, ref)
+ )
+
+ # 3. Invisibly return the value
+ invisible(act$val)
+}
+
top3 <- function(res, dec = 4){
if(is.numeric(res)){
unname(round(res, dec))[1:3]
diff --git a/tests/testthat/test-manip_as.R b/tests/testthat/test-manip_as.R
index c769c868..cb993a05 100644
--- a/tests/testthat/test-manip_as.R
+++ b/tests/testthat/test-manip_as.R
@@ -87,8 +87,7 @@ test_that("conversion of diff_model object works correctly", {
skip_on_ci()
diff <- play_diffusion(ison_brandes)
tidy_diff <- as_tidygraph(diff)
- expect_equal(net_nodes(tidy_diff), net_nodes(ison_brandes))
- expect_equal(net_ties(tidy_diff), net_ties(ison_brandes))
- # expect_true(is_twomode(tidy_diff))
- expect_equal(c(net_nodes(tidy_diff)), max(diff$I))
+ expect_values(net_nodes(tidy_diff), net_nodes(ison_brandes))
+ expect_values(net_ties(tidy_diff), net_ties(ison_brandes))
+ expect_values(net_nodes(tidy_diff), max(diff$I))
})
diff --git a/tests/testthat/test-manip_reformat.R b/tests/testthat/test-manip_format.R
similarity index 95%
rename from tests/testthat/test-manip_reformat.R
rename to tests/testthat/test-manip_format.R
index ebed5fc5..5b09c36d 100644
--- a/tests/testthat/test-manip_reformat.R
+++ b/tests/testthat/test-manip_format.R
@@ -86,11 +86,11 @@ test_that("to_onemode works",{
})
test_that("to_simplex works", {
- expect_true(is_complex(ison_lotr))
- expect_false(is_complex(to_simplex(ison_lotr)))
- expect_false(is_complex(to_simplex(as_igraph(ison_lotr))))
- expect_false(is_complex(to_simplex(as_matrix(ison_lotr))))
- expect_false(is_complex(to_simplex(as_igraph(ison_lotr))))
+ expect_true(is_complex(fict_lotr))
+ expect_false(is_complex(to_simplex(fict_lotr)))
+ expect_false(is_complex(to_simplex(as_igraph(fict_lotr))))
+ expect_false(is_complex(to_simplex(as_matrix(fict_lotr))))
+ expect_false(is_complex(to_simplex(as_igraph(fict_lotr))))
})
test_that("to_unsigned works", {
diff --git a/tests/testthat/test-manip_split.R b/tests/testthat/test-manip_split.R
index 6be03b08..c4a66312 100644
--- a/tests/testthat/test-manip_split.R
+++ b/tests/testthat/test-manip_split.R
@@ -50,11 +50,11 @@ test_that("to_waves works for diff_model objects", {
skip_on_ci()
wave_diff <- play_diffusion(ison_brandes)
expect_length(to_waves(wave_diff), length(wave_diff$t))
- expect_equal(net_nodes(to_waves(wave_diff)[[1]]),
+ expect_values(net_nodes(to_waves(wave_diff)[[1]]),
net_nodes(to_waves(wave_diff)[[12]]))
- expect_equal(net_ties(to_waves(wave_diff)[[1]]),
+ expect_values(net_ties(to_waves(wave_diff)[[1]]),
net_ties(to_waves(wave_diff)[[12]]))
- expect_equal(net_nodes(to_waves(wave_diff)[[1]]),
+ expect_values(net_nodes(to_waves(wave_diff)[[1]]),
net_nodes(ison_brandes))
expect_true(node_attribute(to_waves(wave_diff)[[1]], "Infected")[1])
expect_false(node_attribute(to_waves(wave_diff)[[7]], "Exposed")[1])
diff --git a/tests/testthat/test-manip_transform.R b/tests/testthat/test-manip_transform.R
index b7221db0..d2e51003 100644
--- a/tests/testthat/test-manip_transform.R
+++ b/tests/testthat/test-manip_transform.R
@@ -18,8 +18,8 @@ test_that("matrix projected correctly by rows",{
expect_true(is_weighted(to_mode1(as_edgelist(ison_southern_women))))
expect_true(all(node_names(to_mode1(ison_southern_women)) %in% node_names(ison_southern_women)))
expect_true(length(node_names(to_mode1(ison_southern_women))) != length(node_names(ison_southern_women)))
- expect_equal(length(node_names(to_mode1(ison_southern_women))), length(rownames(as_matrix(ison_southern_women))))
- expect_equal(net_nodes(to_mode1(ison_southern_women, "count")), net_nodes(to_mode1(ison_southern_women, "jaccard")))
+ expect_values(length(node_names(to_mode1(ison_southern_women))), length(rownames(as_matrix(ison_southern_women))))
+ expect_values(net_nodes(to_mode1(ison_southern_women, "count")), net_nodes(to_mode1(ison_southern_women, "jaccard")))
expect_true(is_weighted(to_mode1(ison_southern_women, "pearson")))
expect_false(tie_weights(to_mode1(ison_southern_women, "rand"))[3] == tie_weights(to_mode1(ison_southern_women, "count"))[3])
})
@@ -33,15 +33,15 @@ test_that("matrix projected correctly by columns",{
expect_true(is_weighted(to_mode2(as_edgelist(ison_southern_women))))
expect_true(all(node_names(to_mode2(ison_southern_women)) %in% node_names(ison_southern_women)))
expect_true(length(node_names(to_mode2(ison_southern_women))) != length(node_names(ison_southern_women)))
- expect_equal(length(node_names(to_mode2(ison_southern_women))), length(colnames(as_matrix(ison_southern_women))))
- expect_equal(net_nodes(to_mode2(ison_southern_women, "count")), net_nodes(to_mode2(ison_southern_women, "jaccard")))
+ expect_values(length(node_names(to_mode2(ison_southern_women))), length(colnames(as_matrix(ison_southern_women))))
+ expect_values(net_nodes(to_mode2(ison_southern_women, "count")), net_nodes(to_mode2(ison_southern_women, "jaccard")))
expect_true(is_weighted(to_mode2(ison_southern_women, "pearson")))
expect_false(tie_weights(to_mode2(ison_southern_women, "rand"))[1] == tie_weights(to_mode2(ison_southern_women, "count"))[1])
})
test_that("to matching works", {
sw <- as_edgelist(to_matching(ison_southern_women))
- expect_equal(net_nodes(to_matching(ison_southern_women)),
+ expect_values(net_nodes(to_matching(ison_southern_women)),
net_nodes(ison_southern_women))
expect_true(nrow(sw) == nrow(dplyr::distinct(sw)))
})
diff --git a/tests/testthat/test-mark_nodes.R b/tests/testthat/test-mark_nodes.R
index 4bc89c57..6057dbba 100644
--- a/tests/testthat/test-mark_nodes.R
+++ b/tests/testthat/test-mark_nodes.R
@@ -1,12 +1,35 @@
-test_that("node cuts works", {
- expect_s3_class(node_is_cutpoint(ison_algebra), "node_mark")
+set.seed(1234)
+
+test_that("node_is_cutpoint", {
+ expect_true(exists("node_is_cutpoint"))
+ test_that("returns correct type", {
+ expect_s3_class(node_is_cutpoint(ison_algebra), "node_mark")
+ })
expect_length(node_is_cutpoint(ison_southern_women),
c(net_nodes(ison_southern_women)))
})
-test_that("node isolate works", {
- expect_s3_class(node_is_isolate(ison_brandes), "logical")
- expect_equal(length(node_is_isolate(ison_brandes)), c(net_nodes(ison_brandes)))
+test_that("node_is_isolate", {
+ f <- node_is_isolate
+ expect_true(is.function(f))
+ test <- f(ison_brandes)
+ test_that("returns correct values", {
+ expect_equal(length(test), c(net_nodes(ison_brandes)))
+ })
+ test_that("returns correct type", {
+ expect_s3_class(test, "logical")
+ })
+})
+
+test_that("node_is_fold", {
+ expect_true(exists("node_is_fold"))
+ test <- node_is_fold(create_explicit(A-B, B-C, A-C, C-D, C-E, D-E))
+ test_that("returns correct values", {
+ expect_equal(as.logical(test), c(F,F,T,F,F))
+ })
+ test_that("returns correct type", {
+ expect_s3_class(test, "node_mark")
+ })
})
test_that("node_is_max works", {
@@ -36,9 +59,6 @@ test_that("additional node mark functions work", {
c("FALSE", "FALSE", "TRUE", "TRUE", "FALSE", "FALSE", "FALSE",
"FALSE", "TRUE", "FALSE", "FALSE"))
set.seed(1234)
- expect_equal(as.character(node_is_fold(create_explicit(A-B, B-C, A-C, C-D, C-E, D-E))),
- c("FALSE", "FALSE", "TRUE", "FALSE", "FALSE"))
- set.seed(1234)
expect_equal(as.character(node_is_mentor(ison_adolescents)),
c("FALSE", "TRUE", "TRUE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE"))
set.seed(1234)
diff --git a/tests/testthat/test-measure_centrality.R b/tests/testthat/test-measure_centrality.R
index e5aceab0..836d3158 100644
--- a/tests/testthat/test-measure_centrality.R
+++ b/tests/testthat/test-measure_centrality.R
@@ -67,16 +67,15 @@ test_that("two mode betweenness centrality calculated correctly",{
})
test_that("one mode eigenvector centrality calculated correctly",{
- expect_equal(top3(node_eigenvector(ison_adolescents, normalized = FALSE)), c(0.16, 0.491, 0.529), tolerance = 0.001)
- expect_equal(top3(node_eigenvector(ison_adolescents, normalized = TRUE)), c(0.227, 0.694, 0.748), tolerance = 0.001)
+ # expect_equal(top3(node_eigenvector(ison_adolescents, normalized = FALSE)), c(0.16, 0.491, 0.529), tolerance = 0.001)
+ expect_equal(top3(node_eigenvector(ison_adolescents)), c(0.303, 0.928, 1), tolerance = 0.001)
})
test_that("two mode eigenvector centrality calculated correctly",{
- expect_equal(top3(node_eigenvector(test_mat, normalized = FALSE)), c(0.3185, 0.3004, 0.3536))
- expect_equal(top3(node_eigenvector(test_igr, normalized = FALSE)), c(0.3185, 0.3004, 0.3536))
- expect_equal(bot3(node_eigenvector(test_mat, normalized = FALSE)), c(0.2156, 0.1316, 0.1316))
- expect_equal(bot3(node_eigenvector(test_igr, normalized = FALSE)), c(0.2156, 0.1316, 0.1316))
- expect_equal(top3(node_eigenvector(test_igr, normalized = TRUE)), c(0.4505, 0.4248, 0.5000))
+ expect_equal(top3(node_eigenvector(test_mat)), c(0.9009, 0.8497, 1))
+ expect_equal(bot3(node_eigenvector(test_mat)), c(0.4764, 0.2907, 0.2907))
+ expect_equal(top3(node_eigenvector(test_igr)), c(0.9009, 0.8497, 1))
+ expect_equal(bot3(node_eigenvector(test_igr)), c(0.4764, 0.2907, 0.2907))
})
test_that("node measure class works", {
diff --git a/tests/testthat/test-motif_census.R b/tests/testthat/test-motif_census.R
index 26b05bfe..9b142bd4 100644
--- a/tests/testthat/test-motif_census.R
+++ b/tests/testthat/test-motif_census.R
@@ -33,8 +33,8 @@ test_that("net_triad census works", {
expect_error(net_by_triad(ison_southern_women))
})
-test <- node_by_quad(ison_southern_women)
-test_that("node quad census works", {
+test <- node_by_tetrad(ison_southern_women)
+test_that("node tetrad census works", {
expect_s3_class(test, "node_motif")
expect_equal(test[1,1], 1241)
})
'
+),
answer("B",
message = "This is the equation for an undirected network."),
answer("C",
@@ -177,7 +196,9 @@ question("Which equation is used for measuring density for a directed network:",
)
```
-
+One can calculate the density of the network using the number of nodes
+and the number of ties using the functions `net_nodes()` and `net_ties()`,
+respectively:
```{r dens-explicit, exercise=TRUE, exercise.setup = "separatingnets", purl = FALSE}
@@ -188,7 +209,8 @@ question("Which equation is used for measuring density for a directed network:",
net_ties(tasks)/(net_nodes(tasks)*(net_nodes(tasks)-1))
```
-but we can also just use the `{manynet}` function...
+but we can also just use the `{manynet}` function for calculating the density,
+which always uses the equation appropriate for the type of network...
```{r dens, exercise=TRUE, exercise.setup = "separatingnets", purl = FALSE}
@@ -208,7 +230,7 @@ question("Is this network's density high or low in absolute terms?",
message = "The closer the value is to 1, the more dense the network and the more cohesive the network is as a whole."),
answer("Low",
correct = TRUE,
- message = "The closer the value is to 0, the sparser the network and the less cohesive the network is as a whole. But this is still quite typical density for a relatively small, social network like this one."),
+ message = 'The closer the value is to 0, the sparser the network and the less cohesive the network is as a whole. But this is still quite typical density for a relatively small, social network like this one. 
'),
allow_retry = TRUE
)
```
@@ -218,7 +240,23 @@ the network as a whole.
## Closure
-Next let's calculate _reciprocity_ in the task network.
+In this section we're going to move from generalised measures of cohesion,
+like density, to more localised measures of cohesion.
+These are not only measures of cohesion though,
+but are also often associated with certain mechanisms of closure.
+Closure involves ties being more likely because other ties are present.
+There are two common examples of this in the literature:
+`gloss("reciprocity")`, where a directed tie is often likely to prompt a reciprocating tie,
+and `gloss("transitivity")`, where a directed two-path is likely to be shortened
+by an additional arc connecting the first and third nodes on that path.
+
+
+
+### Reciprocity
+
+First, let's calculate
+`r gloss("reciprocity")`
+in the task network.
While one could do this by hand,
it's more efficient to do this using the `{manynet}` package.
Can you guess the correct name of the function?
@@ -247,7 +285,11 @@ and yet node 16 is both the sender and receiver of most of the task activity.
So our reciprocity measure has taught us something about this network
that might not have been obvious visually.
-And let's calculate _transitivity_ in the task network.
+### Transitivity
+
+And let's calculate
+`r gloss("transitivity")`
+in the task network.
Again, can you guess the correct name of this function?
```{r trans, exercise=TRUE, exercise.setup = "separatingnets", purl = FALSE}
@@ -262,11 +304,12 @@ net_transitivity(tasks)
```{r trans-interp, echo=FALSE, purl = FALSE}
question("What can we say about task closure in this network? Choose all that apply.",
answer("Transitivity for the task network is 0.568",
- correct = TRUE),
+ correct = TRUE,
+ message = '
'),
answer("Transitivity for the task network is -0.568",
message = "Transivitity must be between 0 and 1."),
answer("Transitivity is quite low in this network",
- message = "Transitivity is usually around 0.3 in most social networks."),
+ message = "Transitivity is often around 0.3 in most social networks."),
answer("Transitivity is quite high in this network",
correct = TRUE),
answer("Transitivity is likely higher in the task network than the friendship network",
@@ -276,13 +319,158 @@ question("What can we say about task closure in this network? Choose all that ap
)
```
+## Projection
+
+### A two-mode network
+
+The next dataset, 'ison_southern_women', is also available in `{manynet}`.
+Let's load and graph the data.
+
+```{r setup-women, exercise=TRUE, exercise.setup = "data", purl = FALSE}
+
+```
+
+```{r setup-women-hint-1, purl = FALSE}
+# let's load the data and analyze it
+data("ison_southern_women")
+ison_southern_women
+```
+
+```{r setup-women-hint-2, purl = FALSE}
+graphr(ison_southern_women, node_color = "type")
+graphr(ison_southern_women, "railway", node_color = "type")
+```
+
+```{r setup-women-solution}
+data("ison_southern_women")
+ison_southern_women
+graphr(ison_southern_women, node_color = "type")
+```
+
+### Project two-mode network into two one-mode networks
+
+Now what if we are only interested in one part of the network?
+For that, we can obtain a 'projection' of the two-mode network.
+There are two ways of doing this.
+The hard way...
+
+```{r hardway, exercise=TRUE, exercise.setup = "setup-women", purl = FALSE}
+
+```
+
+```{r hardway-solution}
+twomode_matrix <- as_matrix(ison_southern_women)
+women_matrix <- twomode_matrix %*% t(twomode_matrix)
+event_matrix <- t(twomode_matrix) %*% twomode_matrix
+```
+
+Or the easy way:
+
+```{r easyway, exercise=TRUE, exercise.setup = "setup-women"}
+# women-graph
+# to_mode1(): Results in a weighted one-mode object that retains the row nodes from
+# a two-mode object, and weights the ties between them on the basis of their joint
+# ties to nodes in the second mode (columns)
+
+women_graph <- to_mode1(ison_southern_women)
+graphr(women_graph)
+
+# note that projection `to_mode1` involves keeping one type of nodes
+# this is different from to_uniplex above, which keeps one type of ties in the network
+
+# event-graph
+# to_mode2(): Results in a weighted one-mode object that retains the column nodes from
+# a two-mode object, and weights the ties between them on the basis of their joint ties
+# to nodes in the first mode (rows)
+
+event_graph <- to_mode2(ison_southern_women)
+graphr(event_graph)
+```
+
+`{manynet}` also includes several other options for how to construct the projection.
+The default ("count") might be interpreted as indicating the degree of
+opportunity between nodes that comes from sharing ties to the other mode.
+"jaccard" divides this count by the number of nodes in the other mode that
+to which either of the nodes are tied.
+It can thus be interpreted as opportunity weighted by participation.
+"rand" instead counts both shared ties and shared absences,
+and can thus be interpreted as the degree of behavioural mirroring between the
+nodes.
+Lastly, "pearson" (Pearson's coefficient) and "yule" (Yule's Q) produce
+correlations in ties for valued and binary data respectively.
+
+```{r otherway, exercise=TRUE, exercise.setup = "setup-women", purl = FALSE}
+
+```
+
+```{r otherway-solution}
+to_mode2(ison_southern_women, similarity = "jaccard")
+to_mode2(ison_southern_women, similarity = "rand")
+to_mode2(ison_southern_women, similarity = "pearson")
+to_mode2(ison_southern_women, similarity = "yule")
+```
+
+Let's return to the question of closure.
+First try one of the closure measures we have already treated that gives us
+a sense of shared partners for one-mode networks.
+Then compare this with `net_equivalency()`, which can be used on the original
+two-mode network.
+
+```{r twomode-cohesion, exercise=TRUE, exercise.setup = "easyway", purl = FALSE}
+
+```
+
+```{r twomode-cohesion-hint-1, purl = FALSE}
+# net_transitivity(): Calculate transitivity in a network
+
+net_transitivity(women_graph)
+net_transitivity(event_graph)
+```
+
+```{r twomode-cohesion-hint-2, purl = FALSE}
+# net_equivalency(): Calculate equivalence or reinforcement in a (usually two-mode) network
+
+net_equivalency(ison_southern_women)
+```
+
+```{r twomode-cohesion-solution}
+net_transitivity(women_graph)
+net_transitivity(event_graph)
+net_equivalency(ison_southern_women)
+```
+
+```{r equil-interp, echo=FALSE, purl = FALSE}
+question("What do we learn from this? Choose all that apply.",
+ answer("Transitivity in the women projection is very high.",
+ correct = TRUE),
+ answer("Transitivity in the event projection is very high.",
+ correct = TRUE),
+ answer("Equivalence for the two-mode Southern Women dataset is moderate.",
+ correct = TRUE,
+ message = '
'),
+ answer("Transitivity will often be very high in projected networks.",
+ correct = TRUE),
+ answer("Projection obscures which women are members of which events and vice versa.",
+ correct = TRUE),
+ # random_answer_order = TRUE,
+ allow_retry = TRUE
+)
+```
+
+Try to explain in no more than a paragraph why projection can lead to misleading transitivity measures and what some consequences of this might be.
+
+
+
## Components
Now let's look at the friendship network, 'friends'.
-We're interested here in how many _components_ there are.
+We're interested here in how many
+`r gloss("components", "component")`
+there are.
By default, the `net_components()` function will
return the number of _strong_ components for directed networks.
-For _weak_ components, you will need to first make the network undirected.
+For _weak_ components, you will need to first make the network
+`r gloss("undirected")`.
Remember the difference between weak and strong components?
```{r weak-strong, echo = FALSE, purl = FALSE}
@@ -327,7 +515,7 @@ question("How many components are there?",
message = "There are 3 _weak_ components.",
correct = TRUE),
answer("4",
- message = "There are 4 _strong_ components.",
+ message = 'There are 4 _strong_ components. 
',
correct = TRUE),
answer("5",
message = "There are fewer than 5 components."),
@@ -382,6 +570,113 @@ question("Why is there a difference between the weak and strong components resul
)
```
+## Factions
+
+Components offer a precise way of understanding groups in a network.
+However, they can also ignore some 'groupiness' that is obvious to even a
+cursory examination of the graph.
+The `irps_blogs` network concerns the url links between political blogs
+in the 2004 election.
+It is a big network (you can check below).
+In our experience, it can take a few seconds
+
+```{r blogsize, exercise = TRUE}
+# This is a large network
+net_nodes(irps_blogs)
+# Let's concentrate on just a sample of 490
+blogs <- delete_nodes(irps_blogs, sample(1:1490, 1000))
+graphr(blogs)
+```
+
+But are they all actually linked?
+Even among the smaller sample, there seems to be a number of isolates.
+We can calculate the number of isolates by simply summing `node_is_isolate()`.
+
+```{r blogisolates, exercise = TRUE, exercise.setup = "blogsize"}
+sum(node_is_isolate(blogs))
+```
+
+Since there are many isolates, there will be many components,
+even if we look at weak components and not just strong components.
+
+```{r blogcomp, exercise = TRUE, exercise.setup = "blogsize"}
+net_components(blogs)
+net_components(to_undirected(blogs))
+```
+
+### Giant component
+
+So, it looks like most of the (weak) components are due to isolates!
+How do we concentrate on the main component of this network?
+Well, the main/largest component in a network is called the
+`r gloss("giant component", "giant")`.
+
+```{r blogtogiant, exercise=TRUE, warning=FALSE, fig.width=9, exercise.setup = "blogsize"}
+blogs <- blogs %>% to_giant()
+sum(node_is_isolate(blogs))
+graphr(blogs)
+```
+
+Finally, we have a single 'giant' component to examine.
+However, now we have a different kind of challenge:
+everything is one big hairball.
+And yet, if we think about what we might expect of the structure of a network
+of political blogs, we might not think it is so undifferentiated.
+We might hypothesise that, despite the _graphical_ presentation of a hairball,
+there is actually a reasonable partition of the network into two factions.
+
+### Finding a partition
+
+To find a partition in a network, we use the `node_in_partition()` function.
+All `node_in_*()` functions return a string vector the length of the number
+of nodes in the network.
+It is a string vector because this is how a categorical result is obtained.
+We can assign the result of this function to the nodes in the network
+(because it is the length of the nodes in the network),
+and graph the network using this result.
+
+```{r bloggraph, exercise=TRUE, exercise.setup = "blogtogiant", warning=FALSE, fig.width=9}
+blogs %>% mutate_nodes(part = node_in_partition()) %>%
+ graphr(node_color = "part")
+```
+
+We see from this graph that indeed there seems to be an obvious separation
+between the left and right 'hemispheres' of the network.
+
+## Modularity
+
+But what is the 'fit' of this assignment of the blog nodes into two partitions?
+The most common measure of the fit of a community assignment in a network is
+modularity.
+
+```{r blogmod, exercise=TRUE, exercise.setup = "blogtogiant"}
+net_modularity(blogs, membership = node_in_partition(blogs))
+```
+
+Remember that modularity ranges between 1 and -1.
+How can we interpret this result?
+
+While the partition algorithm is useful for deriving a partition of the network
+into the number of factions assigned,
+it is still an algorithm that tries to maximise modularity.
+Other times we might instead have an empirically collected grouping,
+and we are keen to see how 'modular' the network is around this attribute.
+This only works on categorical attributes, of course,
+but is otherwise quite flexible.
+
+```{r blogmodassign, exercise=TRUE, exercise.setup = "blogtogiant", warning=FALSE, fig.width=9}
+graphr(blogs, node_color = "Leaning")
+net_modularity(blogs, membership = node_attribute(blogs, "Leaning"))
+```
+
+
+
+How interesting.
+Perhaps the partitioning algorithm is not the algorithm that maximises
+modularity after all...
+Perhaps we need to look further and see whether there is another solution here
+that returns an even greater modularity criterion.
+
## Communities
Ok, the friendship network has 3-4 components, but how many 'groups' are there?
@@ -647,132 +942,28 @@ question("What is the difference between communities and components?",
allow_retry = TRUE)
```
-## Projection
-
-### A two-mode network
-
-The next dataset, 'ison_southern_women', is also available in `{manynet}`.
-Let's load and graph the data.
-
-```{r setup-women, exercise=TRUE, exercise.setup = "data", purl = FALSE}
-
-```
-
-```{r setup-women-hint-1, purl = FALSE}
-# let's load the data and analyze it
-data("ison_southern_women")
-ison_southern_women
-```
-
-```{r setup-women-hint-2, purl = FALSE}
-graphr(ison_southern_women, node_color = "type")
-graphr(ison_southern_women, "railway", node_color = "type")
-```
-
-```{r setup-women-solution}
-data("ison_southern_women")
-ison_southern_women
-graphr(ison_southern_women, node_color = "type")
-```
-
-### Project two-mode network into two one-mode networks
-
-Now what if we are only interested in one part of the network?
-For that, we can obtain a 'projection' of the two-mode network.
-There are two ways of doing this.
-The hard way...
-
-```{r hardway, exercise=TRUE, exercise.setup = "setup-women", purl = FALSE}
-
-```
-
-```{r hardway-solution}
-twomode_matrix <- as_matrix(ison_southern_women)
-women_matrix <- twomode_matrix %*% t(twomode_matrix)
-event_matrix <- t(twomode_matrix) %*% twomode_matrix
-```
-
-Or the easy way:
-
-```{r easyway, exercise=TRUE, exercise.setup = "setup-women", purl = FALSE}
-
-```
-
-```{r easyway-hint-1, purl = FALSE}
-# women-graph
-# to_mode1(): Results in a weighted one-mode object that retains the row nodes from
-# a two-mode object, and weights the ties between them on the basis of their joint
-# ties to nodes in the second mode (columns)
-
-women_graph <- to_mode1(ison_southern_women)
-graphr(women_graph)
+## Free play
-# note that projection `to_mode1` involves keeping one type of nodes
-# this is different from to_uniplex above, which keeps one type of ties in the network
-```
+
-```{r easyway-hint-2, purl = FALSE}
-# event-graph
-# to_mode2(): Results in a weighted one-mode object that retains the column nodes from
-# a two-mode object, and weights the ties between them on the basis of their joint ties
-# to nodes in the first mode (rows)
+We've looked here at the `irps_blogs` dataset.
+Now have a go at the `irps_books` dataset.
+What is the density? Does it make sense to investigate reciprocity,
+transitivity, or equivalence? How can we interpret the results?
+How many components in the network? Is there a strong factional structure?
+Which community detection algorithm returns the highest modularity score,
+or corresponds best to what is in the data or what you see in the graph?
-event_graph <- to_mode2(ison_southern_women)
-graphr(event_graph)
+```{r freeplay, exercise = TRUE, fig.width=9}
+irps_books
```
-```{r easyway-solution}
-women_graph <- to_mode1(ison_southern_women)
-graphr(women_graph)
-event_graph <- to_mode2(ison_southern_women)
-graphr(event_graph)
-```
+
-`{manynet}` also includes several other options for how to construct the projection.
-Please see the help file for more details.
-
-```{r otherway, exercise=TRUE, exercise.setup = "setup-women", purl = FALSE}
-
-```
-
-```{r otherway-solution}
-graphr(to_mode2(ison_southern_women, similarity = "jaccard")) + ggtitle("Jaccard") +
-graphr(to_mode2(ison_southern_women, similarity = "rand")) + ggtitle("Rand") +
-graphr(to_mode2(ison_southern_women, similarity = "pearson")) + ggtitle("Pearson") +
-graphr(to_mode2(ison_southern_women, similarity = "yule")) + ggtitle("Yule's Q")
-```
-
-Which women/events 'bind' which events/women?
-Let's return to the question of cohesion.
-
-```{r twomode-cohesion, exercise=TRUE, exercise.setup = "setup-women", purl = FALSE}
-
-```
-
-```{r twomode-cohesion-hint-1, purl = FALSE}
-# net_equivalency(): Calculate equivalence or reinforcement in a (usually two-mode) network
-
-net_equivalency(ison_southern_women)
-```
-
-```{r twomode-cohesion-hint-2, purl = FALSE}
-# net_transitivity(): Calculate transitivity in a network
-
-net_transitivity(women_graph)
-net_transitivity(event_graph)
-```
-
-```{r twomode-cohesion-solution}
-net_equivalency(ison_southern_women)
-net_transitivity(women_graph)
-net_transitivity(event_graph)
-```
+## Glossary
-What do we learn from this?
+
-## Task/Unit Test
+Here are some of the terms that we have covered in this module:
-1. Produce a plot comparing 3 community detection procedures used here on a
-(women) projection of the 'ison_southern_women' dataset. Identify which you prefer, and explain why.
-2. Explain in no more than a paragraph why projection can lead to misleading transitivity measures.
-3. Explain in no more than a paragraph how structural balance might lead to group identity.
+`r print_glossary()`
diff --git a/inst/tutorials/tutorial4/community.html b/inst/tutorials/tutorial4/community.html
index 65156ab7..14a3665a 100644
--- a/inst/tutorials/tutorial4/community.html
+++ b/inst/tutorials/tutorial4/community.html
@@ -112,6 +112,17 @@
+
For this session, we're going to use the "ison_algebra" dataset included in the `{manynet}` package.
Do you remember how to call the data?
Can you find out some more information about it via its help file?
@@ -77,24 +79,18 @@ ____ <- to_uniplex(ison_algebra, _____)
```
```{r separatingnets-hint-4, purl = FALSE}
-# Now, let's compare the each attribute's graph, side-by-side by using "+"
-# Note: using "/" after each graph will order them vertically; however, it might not be best way
-# See for example:
-gfriend <- graphr(friends) + ggtitle("Friendship")
-gfriend + gsocial + gtask
+graphr(friends) + ggtitle("Friendship")
```
```{r separatingnets-solution}
friends <- to_uniplex(ison_algebra, "friends")
-gfriend <- graphr(friends) + ggtitle("Friendship")
+graphr(friends) + ggtitle("Friendship")
social <- to_uniplex(ison_algebra, "social")
-gsocial <- graphr(social) + ggtitle("Social")
+graphr(social) + ggtitle("Social")
tasks <- to_uniplex(ison_algebra, "tasks")
-gtask <- graphr(tasks) + ggtitle("Task")
-
-gfriend + gsocial + gtask
+graphr(tasks) + ggtitle("Task")
```
Note also that these are weighted networks.
@@ -120,18 +116,21 @@ question("If we interpret ties with higher weights as strong ties, and lesser we
Our first question for this network, is where innovation and creative ideas
might be expected to appear.
+There are a number of theories that associate innovation or novelty with
+structural position.
```{r structinnov-qa, echo=FALSE, purl = FALSE}
question("Which network concepts are associated with innovation?",
answer("Structural holes",
correct = TRUE,
- message = learnr::random_praise()),
+ message = "Being positioned in a structural hole is said to be a brokerage position that brings with it information arbitrage possibilities."),
answer("Structural folds",
- correct = TRUE),
+ correct = TRUE,
+ message = "Being positioned in a structural fold, with in-group membership in multiple groups, can provide not only information arbitrage possibilities, but also the standing in the target group to introduce novelty successfully."),
answer("Structural balance",
message = learnr::random_encouragement()),
answer("Structural equivalence",
- message = learnr::random_encouragement()),
+ message = '
'),
answer("Structuralism",
message = learnr::random_encouragement()),
random_answer_order = TRUE,
@@ -227,6 +226,8 @@ about where innovation might occur within this network?
## Structural Equivalence
+
+
Next we might ask ourselves what (other) roles there are in the network?
We want to know who plays what role in this algebra class.
Let us begin with structural equivalence.
@@ -491,6 +492,8 @@ but here it essentially just reports nodes' identity.
## Blockmodelling
+
+
### Summarising profiles
Ok, so now we have a result from establishing nodes' membership in structurally equivalent classes.
@@ -506,6 +509,8 @@ While this plot adds the structurally equivalent classes information to our earl
it doesn't really help us understand how the classes relate.
That is, we might be less interested in how the individuals in the different classes relate, and more interested in how the different classes relate in aggregate.
+
+
One option that can be useful for characterising what
the profile of ties (partners) is for each position/equivalence class
is to use `summary()`.
@@ -535,7 +540,7 @@ it takes a bit to look through what varies between the different classes
as 'blocked'.
But only four rows (the four structurally equivalent classes, according to the default).
-Another way to do this is to plot the blockmodel as a whole.
+Another way to do this is to plot the `gloss("blockmodel")` as a whole.
Passing the `plot()` function an adjacency/incidence matrix
along with a membership vector allows the matrix to be sorted and framed
(without the membership vector, just the adjacency/incidence matrix is plotted):
@@ -558,11 +563,11 @@ plot(as_matrix(alge),
membership = node_in_structural(alge))
# plot the blockmodel for the friends, tasks, and social networks separately
-plot(as_matrix(friends),
- membership = node_in_structural(alge)) +
-plot(as_matrix(tasks),
- membership = node_in_structural(alge)) +
-plot(as_matrix(social),
+plot(as_matrix(to_uniplex(alge, "friends")),
+ membership = node_in_structural(alge))
+plot(as_matrix(to_uniplex(alge, "tasks")),
+ membership = node_in_structural(alge))
+plot(as_matrix(to_uniplex(alge, "social")),
membership = node_in_structural(alge))
```
@@ -572,17 +577,17 @@ This can help us interpret the general relationships between classes.
For example, when we plot the friends, tasks, and social networks using the structural equivalence results,
we might characterise them like so:
-- The first group work together only in reciprocal pairs on tasks,
-preferring to approach the nerd but also those of the other two roles.
-While they hang out with each other socially quite a bit, friendship from groups 2 and 3 are preferred.
-- The second group also work together only in reciprocal pairs,
-preferring to work collaboratively with group 1 or also the nerd.
-They also tend to count those from group 1 as friends,
-and hang out with everyone else but themselves.
-- The third group will work with either some in group 1 and 3, or 2,
-but again prefer the nerd for task advice.
-They are pretty good friends with each other though,
-and pretty happy to socialise with everyone.
+- The first group (of 6) are a bit of a mix: there seem to be two popular friends,
+one that strongly reciprocates and the other that nominates no friendships but
+seems to nominate others in the group as social contacts instead.
+The first group rely heavily on the nerd for advice.
+- The second group (of 5) seem to be strongly reciprocal in friendship and
+social together, lightly advise each other but mostly go to the nerd for advice.
+- The third group (of 4) are also strongly reciprocal in friendship,
+but also sometimes nominate some in groups one and two as friends too.
+There is at least a pair that often hang out together socially,
+but this group do not hang out with the nerd much nor ask them for advice
+as much as members of the other groups.
- The nerd is a loner, no friends,
but everyone hangs out with them for task advice.
@@ -590,7 +595,7 @@ but everyone hangs out with them for task advice.
Lastly, we can consider how _classes_ of nodes relate to one another in a blockmodel.
Let's use the 4-cluster solution on the valued network (though binary is possible too)
-to create a _reduced graph_.
+to create a `r gloss("reduced graph","reduced")`.
A reduced graph is a transformation of a network such that
the nodes are no longer the individual nodes but the groups of one or more nodes as a class,
and the ties between these blocked nodes can represent the sum or average tie between these classes.
@@ -605,3 +610,22 @@ bm <- bm %>% as_tidygraph %>%
mutate(name = c("Freaks", "Squares", "Nerds", "Geek"))
graphr(bm)
```
+
+## Free play
+
+Now try to find the regularly equivalent classes in the `ison_lawfirm` dataset.
+As this is a multiplex network, you can make this a uniplex network first.
+
+```{r freeplay, exercise = TRUE}
+
+```
+
+An extension can be to also explore automorphically equivalent classes.
+
+## Glossary
+
+Here are some of the terms that we have covered in this module:
+
+`r print_glossary()`
+
+
diff --git a/inst/tutorials/tutorial5/position.html b/inst/tutorials/tutorial5/position.html
index 53dfb3ba..21b41596 100644
--- a/inst/tutorials/tutorial5/position.html
+++ b/inst/tutorials/tutorial5/position.html
@@ -112,6 +112,7 @@