Social Network Analysis: Community Detection

This is the era of big data. Our daily social life, we deal with many networks data and Community detection is specially tailored for network analysis. Social Network Analysis (SNA) is a field of study that examines the structure and behavior of networks of interconnected entities, such as individuals, organizations, or websites. One of the key tasks in SNA is community detection, which involves identifying groups of nodes (individuals or entities) within a network that are more densely connected to each other than to nodes outside their group. These groups are often referred to as "communities" or "clusters," and they represent subsets of nodes in the network that have some common characteristics or interests. On the other hand, Clustering is a machine learning technique in which similar data points are grouped into the same cluster based on their attributes. In this project, we will investigate and apply different Community Detection algorithms (Modularity-Based Methods, Hierarchical Clustering and Graph Partitioning) in social network data.