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:

1. Identifying, for example, the most influential people in a social network,
2. Key infrastructure nodes in urban networks,
3. Important pages on the web,
4. Nodes that spread information across the network,
5. Nodes that can cause/prevent epidemics, and,
6. 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.