NI-Louvain: A novel algorithm to detect overlapping communities with influence analysis

Abstract

Community detection is an important area of research in social media mining. Numerous algorithms have been developed to detect disjoint, overlapping, and dynamic communities in a network. However, most of the existing algorithms do not consider the influence of each node in a group, which may be different and can affect the result of community detection. In this paper, we have proposed a novel Louvain-based algorithm named “NI-Louvain,” which considers the influence of each node in a group that not only detects community but also most influential nodes in the community. The proposed algorithm works in three steps. First, the input graph is processed to reduce its density. This is done by calculating cliques from the graph. In second step, Louvain's multilevel algorithm is applied to this clique graph. The resultant community obtained has a better modularity value than the communities obtained from existing algorithms like edge betweenness, label propagation, leading eigen, Louvain, fast greedy, walktrap, and infomap. In the third step, the nodes of resultant communities are transformed back to the nodes of the original graph. This step is beneficial in detecting overlapping communities, fuzzy membership of each node, most influential node in each community formed, and outlier nodes.