Improving Louvain Algorithm by Leveraging Cliques for Community Detection

Abstract

Community detection is one of the most important fields that help us in understand and analyze the structure of social networks. It is a tool to identify closely related groups in terms of social relations or common interests. In fact, community detection can be applied in social media, web clients, or e-commerce. For this purpose, the traditional Louvain algorithm is used for community detection as a suitable algorithm, since it provides fast, efficient and robust community detection on large static networks. However, the high computing complexity of this algorithm is a motivation of this work. Initially, the existing cliques and the other nodes which have not included in cliques are considered as separated communities instead of considering each node in the network is a community as in the traditional method, then the gain of integrating neighboring communities is calculated. A specific research methodology is followed to ensure that the work is rigorous in achieving the aim of the work. In synthetic and real-world data, the traditional and improved algorithms had to be applied to record the results, then analyze them. Experimentally, the results prove the execution time has reduced if it is compared with the traditional algorithm while preserving the quality of partitions at the same time somewhat.