Social Network Analysis in Python Networks today are part of our everyday life. Let's learn how to visualize and understand a social network in Python using Networks

The dataset you are referring to is the Facebook Social Circles Dataset, which is part of a collection of social network datasets. This dataset was collected by analyzing ego networks on Facebook, where an ego network is defined as a focal node (the ego) and all the nodes (friends) connected to it, along with the links (friendships) between these friends. The key aspects of this dataset include:

 - Node Features: Information about individual users, although anonymized. - Circles: Groups of friends, similar to how Facebook allows users to organize friends into different lists. - Ego Networks: Networks centered around a specific user (the ego), including that user's friends and the connections between them. 

Key Statistics:

Nodes: 4039 (representing users) Edges: 88234 (representing friendships) Clustering Coefficient: 0.6055 (indicating a relatively high level of clustering) Triangles: 1.61 million (showing the number of friend groups that are fully connected) Diameter: 8 (the longest shortest path between any two nodes) Effective Diameter: 4.7 (90th percentile of the shortest path lengths between nodes) 

https://snap.stanford.edu/data/ego-Facebook.html

1- Betweenness Centrality
Betweenness centrality is defined as a measure of how often a node lies on the shortest path between all pairs of nodes in a network

python scripts/betweenness_centrality.py

2- Degree Centrality

python scripts/graph_degree_centrality.py

3- Closeness Centrality
4- Eeigenvector Centrality
5- Find shortest path
6- Find all neighbors the nodes
7- Degree Grapg
8- K-clique
9- K-core
10- pagerank