Community Detection Methods in Social Network Analysis

Abstract

Extracting insights from Social Network has been on trend for its contribution to various research domains to solve real-world applications such as in business, bioscience, marketing, etc. The ability to structure social network as a graph model with nodes as vertices and edges as links has made easier to understand the flow of information in a network and also figure out the different types of relationship existing between the nodes. Community detection is one of the ways to study and uncover the nodes exhibiting similar properties into a separate cluster. With certainty, one can deduce that nodes with similar interest and properties are likely to have frequent interactions and are also in close proximity with each other forming a community, such community can be represented as functional units of the huge social network system making it easier to study the graph as a whole. Understanding the complex social network as a set of communities can also help us to identify meaningful substructures hidden within it, which are often predominated by the superior communities to excavate people’s views, track information propagation, etc. This paper will present the different ways in which one can discover the communities existing in social network graphs based on several community detection methods.