Community Detection Based on an Improved Modularity
Community detection is a very popular research topic in network science nowadays. Various categories of community detection algorithms have been proposed, such as graph partitioning, hierarchical clustering, partitional clustering, and so on. Among these algorithms, modularity-based approaches obtain more attention because modularity is a main criterion to evaluate community partitions. However, current modularity only measures the intra-links within communities and rarely considers the inter-links between them. In this paper, we encode both the intra-links and inter-links in an optimization framework to improve the modularity. The partitions can be computed by the greedy algorithm which utilizes the similar simulated annealing technique. The experimental results on four public datasets demonstrate that our improved modularity can reduce the links between communities, and achieve better performance than the original modularity.
community detection improved modularity greedy algorithm