Social Network Analysis in Python using NetworkX. The networks depict the various properties of a class of 59 students and their connections on social media.
In Network Analysis, indicators of Centrality identify the most important vertices within a network. Its applications include:
- Identifying, for example, the most influential people in a social network,
- Key infrastructure nodes in urban networks,
- Important pages on the web,
- Nodes that spread information across the network,
- Nodes that can cause/prevent epidemics, and,
- Nodes that are crucial to keep the network from breaking up.
Degree Centrality assigns an importance score based on the number of ties each actor in the network has:
“More no. of ties = More important”
It answers the immediate question – How many people in the network are you directly connected to?
Closeness Centrality is a measure of the degree to which an individual is near all other individuals (the number of hops) in a network. It is used for finding the individuals who are best placed to influence the entire network most quickly – good ‘broadcasters’.
“Close to everyone in the network = More important”
It answers the immediate question - How close are you to every person in the network?
Betweenness Centrality is the measure of how many times a particular node comes in between the shortest path between any other two nodes.
“People that act as bridges between other people = More important”
It answers the immediate question - Who is important for the flow of information in a network?
Removal of a node with a high betweenness centrality would result in the disruption of communication across the entire network.
For a more detailed analysis and explanation, please check out the PRESENTATION.pdf file in the repository.