Community Detection in Network: Algorithmic Approaches with Python Programming

Community detection is the identification of different communities or groups that exist within a network. This is useful in social network analysis (SNA) or what is great is performing whole network analysis (WNA), where humans interact with others as part of their various communities, but these approaches are not limited to the study of humans. These methods are to investigate any type of node that interacts closely with other nodes, whether those nodes are animals, hashtags, websites, or any other type of node in the network. In this work, we zoom in on communities that exist in a network. Community detection is a clear, concise, and appropriate name for what we are doing. Communities in the network would be worth exploring and understanding for further purposes. There are several methods and different approaches to detect community, but in this paper, I use two efficient methods to detect whole network which are named Louvain Method (LM) and Girvan-Newman Method (GNM). With LM, we can build a fast algorithm that is effective at community detection in massive networks and optimize the algorithm for better results. Using the GNM, a better approach that can identify the least number of edges that could be cut would result in a split network. We could do this by making an algorithm looking for the edges that the greatest number of shortest paths pass through.