Comparative Analysis of Community Detection Algorithms in Biological Networks

Community detection in biological networks is crucial for understanding complex interactions among biological entities. This research focuses on performing community detection using several algorithms such as Kernighan lin bisection algorithm, Louvain algorithm, Girvan Newman algorithm, Fast Greedy algorithm, and Asynchronous fluid community algorithm on various biological datasets. We evaluated the modularity and partition quality for all the communities using all these algorithms separately and did a comparative analysis on the results. Using those results we were able to identify which algorithm is more efficient and scalable in performing the community detection for biological networks.