Joint Community and Structural Hole Spanner Detection via Harmonic Modularity
Lifang He*, ; CHUN-TA LU, UIC; Jiaqi Ma, Tsinghua University; Jianping Cao, NUDT; Linlin Shen, ; Philip S. Yu, UI Chicago
Detecting communities (or modular structures) and structural hole spanners, the nodes bridging diﬀerent communities in a network, are two essential tasks in the realm of network analytics. Due to the topological nature of com-munities and structural hole spanners, these two tasks are naturally tangled with each other, while there has been little synergy between them. In this paper, we propose a novel harmonic modularity method to tackle both tasks simultaneously. Speciﬁcally, we apply a harmonic function to mea-sure the smoothness of community structure and to obtain the community indicator. We then investigate the sparsity level of the interactions between communities, with particular emphasis on the nodes connecting to multiple communities, to discriminate the indicator of SH spanners and assist the community guidance. Extensive experiments on real-world networks demonstrate that our proposed method outperforms several state-of-the-art methods in the community detection task and also in the SH spanner identiﬁcation task (even the methods that require the supervised community information). Furthermore, by removing the SH spanners spotted by our method, we show that the quality of other community detection methods can be further improved.
Filed under: Graph Mining and Social Networks