CLOSE: Local Community Detection by LOcal Structure Expansion in a Complex Network

Abstract

There have been more and more researches on community discovery in complex networks. Expanding of a source node into a community is one of the most successful methods for local community detection, especially when the global structure of the network is not accessible. In this paper, we propose CLOSE algorithm, Local Community Detection by LOcal Structure Expansion, based on the local expansion technique in the community detection. In CLOSE, we propose a novel connective function to identify a better source node. The node is in the center of a highly connected component of a graph. CLOSE selects a group of nodes instead of a single node to be the seed for the expansion of a local community. In addition, using the neighboring group can identify a suitable community for a hub node. Moreover, the expansion strategy is based on the label propagation technique instead of local community measurements. In experiments, we compare the performance of CLOSE with previous methods both on synthetic networks from the LFR Benchmark and real-world networks. We also examine the merit of the source node selection strategy. Both source node selection and community detection in CLOSE outperform previous algorithms.