Social-Media-Analysis

The project aims at :

• Applying algorithms based on graph theory to compute the centrality property for each node in the undirected graph.
• visualization of the resultant undirected graphs.

1. Centrality in graphs :-

Centrality has many definitions. Each definition gives scores to nodes in different ways. we will discuss three types of centrality.

i. Degree Centrality:

Degree centrality is the simplest centrality measure to compute. Recall that a node's degree is simply a count of how many social connections (i.e., edges) it has.The degree centrality for a node is simply its degree.

Example:

ii. Closeness centrality:

Closeness centrality indicates how close a node is to all other nodes in the network. It is calculated as the average of the shortest path length from the node to every other node in the network.

iii. Betweennes centrality:

Betweenness centrality measures the number of times a node lies on the shortest path between other nodes.

2. Graph visualization :-

This project aims to show you the visual picture of your input weighted undirected graph "combintion of nodes and weighted edges".

"you can see more details of the used tool (in visualization) and algorithms implementation in their specifc branches in REPO"