Expanding Louvain Algorithm for Clustering Relationship Formation

Abstract

Community detection is a method to determine and to discover the existence of cluster or group that share the same interest, hobbies, purposes, projects, lifestyles, location or profession. There are some example of community detection algorithms that have been developed, such as strongly connected components algorithm, weakly connected components, label propagation, triangle count and average clustering coefficient, spectral optimization, Newman and Louvain modularity algorithm. Louvain method is the most efficient algorithm to detect communities in large scale network. Expansion of the Louvain Algorithm is carried out by forming a community based on connections between nodes (users) which are developed by adding weights to nodes to form clusters or referred to as clustering relationships. The next step is to perform weighting based on user relationships using a weighting algorithm that is formed by considering user account activity, such as giving each other recommendation comments, or to decide whether the relationship between the followers and the following is exist or not. The results of this study are the best modularity created with a value of 0.879 and the cluster test is 0.776.