Title: BASIC: Bipartite Assisted Spectral-clustering for Identifying Communities in Large-scale Networks Show Chinese title

Title: 基于二分图辅助谱聚类的大规模网络社区识别方法

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, we propose a Bipartite Assisted Spectral-clustering approach for Identifying Communities (BASIC), which incorporates the bipartite network information into the community structure learning of the primary network. The accuracy and stability enhancement of BASIC is validated theoretically on the basis of the degree-corrected stochastic block model framework, as well as numerically through extensive simulation studies. We rigorously study the convergence rate of BASIC even under weak signal scenarios and prove that BASIC yields a tighter upper error bound than that based on the primary network information alone. We utilize the proposed BASIC method to analyze the newly collected large-scale academic network dataset from statistical papers. During the author collaboration network structure learning, we incorporate the bipartite network information 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. Show Chinese abstract

Abstract: 社区检测，即专注于恢复网络内部的组结构，在网络分析中是一项关键且基础的任务。然而，当社区信号较弱时，检测过程可能会非常具有挑战性且不稳定。 受到从Web of Science新收集的大规模学术网络数据集（包括多层网络信息）的启发，我们提出了一个双部图辅助谱聚类方法来识别社区（BASIC），该方法将主网络的双部网络信息纳入到社区结构的学习中。 基于度校正随机块模型框架，理论验证了BASIC在准确性和稳定性上的提升，同时通过广泛的模拟研究进行了数值验证。 我们严格研究了即使在弱信号情况下BASIC的收敛速度，并证明BASIC比仅基于主网络信息的方法提供了更紧的误差上界。 我们利用所提出的BASIC方法分析了来自统计学论文的新收集的大规模学术网络数据集。 在作者合作网络结构学习过程中，我们结合了作者-论文、作者-机构和作者-地区关系中的双部网络信息。 从统计和解释的角度来看，这些双部网络极大地有助于识别主协作网络中的社区。