A1172
Title: BASIC: Bipartite assisted spectral-clustering for identifying communities in large-scale networks
Authors: Jingyuan Liu - Xiamen University (China) [presenting]
Abstract: Community detection, which focuses on recovering the group structure within networks, is a crucial and fundamental task in network analysis. However, the detection process can be quite challenging and unstable when community signals are weak. Motivated by a newly collected large-scale academic network dataset from the Web of Science, which includes multi-layer network information, a bipartite-assisted spectral-clustering approach for identifying communities (BASIC) is proposed, which incorporates the bipartite network information into the community structure learning of the primary network. The accuracy and stability enhancement of BASIC are validated theoretically and numerically on the basis of the degree-corrected stochastic block model framework through extensive simulation studies. The convergence rate of BASIC is rigorously studied even under weak signal scenarios, and it has been proven that BASIC yields a tighter upper error bound than that based on the primary network information alone. The proposed BASIC method is utilized to analyze the newly collected large-scale academic network dataset from statistical papers. During the author collaboration network structure learning, the bipartite network information is incorporated from author-paper, author-institution, and author-region relationships. From both statistical and interpretative perspectives, these bipartite networks greatly aid in identifying communities within the primary collaboration network.
