Detecting network communities via greedy expanding based on local superiority index

Abstract

Community detection is a significant and challenging task in network science. Nowadays, plenty of attention has been paid on local methods for community detection. Greedy expanding is a popular and efficient class of local algorithms, which typically starts from some selected central nodes and expands those nodes to obtain provisional communities by optimizing a certain quality function. In this paper, we propose a novel index, called local superiority index (LSI), to identify central nodes. In the process of expansion, we apply the fitness function to estimate the quality of provisional communities and ensure that all provisional communities must be weak communities. Evaluation based on the normalized mutual information suggests: (1) LSI is superior to the global maximal degree index and the local maximal degree index on most considered networks; (2) The greedy algorithm based on LSI is better than the classical fast algorithm on most considered networks.

References in corpus (9)

• Fast unfolding of communities in large networks
• Cooperative Game Theory Approaches for Network Partitioning
• Benchmark graphs for testing community detection algorithms
• Detecting the overlapping and hierarchical community structure of complex networks
• Community Structure in Jazz
• The Rich-Club Phenomenon In The Internet Topology
• Mixture models and exploratory analysis in networks
• Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity
• Local Leaders in Random Networks