Fix networkx issue (#268)

* initial commit

* changed python version

* fixed matrix multiply

.github/workflows/test.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.8', '3.9', '3.10', '3.11']
     steps:
     - uses: actions/checkout@v2
 
@@ -57,7 +57,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu, macos, windows]
-        python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.8', '3.9', '3.10', '3.11']
     steps:
     - uses: actions/checkout@v2
 

README.md

@@ -22,7 +22,7 @@ Sign up for our [mailing list](http://eepurl.com/igE6ez) and follow XGI on [Twit
   - [Other Resources](#other-resources)
 
 ## Installation
-XGI runs on Python 3.7 or higher.
+XGI runs on Python 3.8 or higher.
 
 To install the latest version of XGI, run the following command:
 ```sh

docs/source/about.rst

@@ -34,7 +34,7 @@ If that command does not work, you may try the following instead
 
    pip install -e .\[all\]
 
-XGI was developed and tested for Python 3.7-3.10 on Mac OS, Windows, and Ubuntu.
+XGI was developed and tested for Python 3.8-3.11 on Mac OS, Windows, and Ubuntu.
 
 
 Academic References

docs/source/index.rst

@@ -91,7 +91,7 @@ If that command does not work, you may try the following instead
 
    pip install -e .\[all\]
 
-XGI was developed and tested for Python 3.7-3.11 on Mac OS, Windows, and Ubuntu.
+XGI was developed and tested for Python 3.8-3.11 on Mac OS, Windows, and Ubuntu.
 
 Corresponding Data
 ==================

long_description.rst

@@ -9,7 +9,7 @@ and study of the structure, dynamics, and functions of complex systems with grou
 
 Installation
 ------------
-XGI runs on Python 3.7 or higher.
+XGI runs on Python 3.8 or higher.
 
 To install the latest version of XGI, run the following command::
 

requirements/default.txt

@@ -1,6 +1,6 @@
 numpy>=1.19
 scipy>=1.5
 pandas>=1.0
-networkx>=2.0,<3.0
+networkx>=2.7
 requests>=2.0
 matplotlib>=3.3

setup.py

@@ -5,8 +5,8 @@
 
 __version__ = "0.5.2"
 
-if sys.version_info < (3, 7):
-    sys.exit("XGI requires Python 3.7 or later.")
+if sys.version_info < (3, 8):
+    sys.exit("XGI requires Python 3.8 or later.")
 
 name = "xgi"
 

tests/linalg/test_matrix.py

@@ -1,6 +1,6 @@
 import numpy as np
 import pytest
-from scipy.sparse import csr_matrix
+from scipy.sparse import csr_array
 from scipy.sparse.linalg import norm as spnorm
 
 import xgi
@@ -89,7 +89,7 @@ def test_incidence_matrix(edgelist1, edgelist3, edgelist4):
     assert I6[node_dict6[8], edge_dict6[3]] == 1
 
     data = xgi.incidence_matrix(H1)
-    assert type(data) == csr_matrix
+    assert type(data) == csr_array
 
     H7 = xgi.empty_hypergraph()
     H7.add_nodes_from(range(8))  # disconnected node 0
@@ -158,7 +158,7 @@ def test_adjacency_matrix(edgelist1, edgelist4):
     node_dict2 = {k: v for v, k in node_dict.items()}
 
     data = xgi.adjacency_matrix(H2)
-    assert type(data) == csr_matrix
+    assert type(data) == csr_array
 
     for i in range(np.size(A1, axis=0)):
         assert A1[i, i] == 0
@@ -390,7 +390,7 @@ def test_clique_motif_matrix(edgelist4):
     node_dict1 = {k: v for v, k in node_dict.items()}
 
     data = xgi.clique_motif_matrix(H1)
-    assert type(data) == csr_matrix
+    assert type(data) == csr_array
 
     for i in range(np.size(W1, axis=0)):
         assert W1[i, i] == 0

xgi/algorithms/centrality.py

@@ -220,8 +220,8 @@ def node_edge_centrality(
     check = np.inf
 
     for iter in range(max_iter):
-        u = np.multiply(x, g(I * f(y)))
-        v = np.multiply(y, psi(I.T * phi(x)))
+        u = np.multiply(x, g(I @ f(y)))
+        v = np.multiply(y, psi(I.T @ phi(x)))
         new_x = u / norm(u)
         new_y = v / norm(v)
 

xgi/convert.py

@@ -5,7 +5,16 @@
 import pandas as pd
 from networkx.algorithms import bipartite
 from numpy import matrix, ndarray
-from scipy.sparse import coo_matrix, csc_matrix, csr_matrix, lil_matrix
+from scipy.sparse import (
+    coo_array,
+    coo_matrix,
+    csc_array,
+    csc_matrix,
+    csr_array,
+    csr_matrix,
+    lil_array,
+    lil_matrix,
+)
 
 from .classes import (
     Hypergraph,
@@ -84,7 +93,19 @@ def convert_to_hypergraph(data, create_using=None):
         from_hyperedge_dict(data, create_using)
 
     elif isinstance(
-        data, (ndarray, matrix, csr_matrix, csc_matrix, coo_matrix, lil_matrix)
+        data,
+        (
+            ndarray,
+            matrix,
+            csr_array,
+            csc_array,
+            coo_array,
+            lil_array,
+            csr_matrix,
+            csc_matrix,
+            coo_matrix,
+            lil_matrix,
+        ),
     ):
         from_incidence_matrix(data, create_using)
 
@@ -108,7 +129,7 @@ def convert_to_graph(H):
     """
 
     A = adjacency_matrix(H)  # This is unweighted by design
-    G = nx.from_scipy_sparse_matrix(A)
+    G = nx.from_scipy_sparse_array(A)
     G = nx.relabel_nodes(G, {i: node for i, node in enumerate(H.nodes)})
     return G
 
@@ -161,7 +182,19 @@ def convert_to_simplicial_complex(data, create_using=None):
         # edge dict in the form we need
         raise XGIError("Cannot generate SimplicialComplex from simplex dictionary")
     elif isinstance(
-        data, (ndarray, matrix, csr_matrix, csc_matrix, coo_matrix, lil_matrix)
+        data,
+        (
+            ndarray,
+            matrix,
+            csr_array,
+            csc_array,
+            coo_array,
+            lil_array,
+            csr_matrix,
+            csc_matrix,
+            coo_matrix,
+            lil_matrix,
+        ),
     ):
         # incidence matrix
         raise XGIError(
@@ -382,7 +415,7 @@ def from_incidence_matrix(d, create_using=None, nodelabels=None, edgelabels=None
     incidence_matrix
     to_incidence_matrix
     """
-    I = coo_matrix(d)
+    I = coo_array(d)
     n, m = I.shape
 
     if nodelabels is None:
@@ -447,7 +480,7 @@ def to_incidence_matrix(H, sparse=True, index=False):
 
     Returns
     -------
-    numpy.ndarray or scipy csr_matrix
+    numpy.ndarray or scipy csr_array
         The incidence matrix
     dict
         The dictionary mapping indices to node IDs, if index is True

xgi/linalg/matrix.py

@@ -2,7 +2,7 @@
 from warnings import warn
 
 import numpy as np
-from scipy.sparse import csr_matrix, diags
+from scipy.sparse import csr_array, diags
 
 __all__ = [
     "incidence_matrix",
@@ -40,7 +40,7 @@ def incidence_matrix(
 
     Returns
     -------
-    I: numpy.ndarray or scipy csr_matrix
+    I: numpy.ndarray or scipy csr_array
         The incidence matrix, has dimension (n_nodes, n_edges)
     rowdict: dict
         The dictionary mapping indices to node IDs, if index is True
@@ -85,7 +85,7 @@ def incidence_matrix(
                     data.append(0)
                     rows.append(node_dict[node])
                     cols.append(edge_dict[edge])
-        I = csr_matrix((data, (rows, cols)))
+        I = csr_array((data, (rows, cols)))
     else:
         # Create an np.matrix
         I = np.zeros((num_nodes, num_edges), dtype=int)