论文标题
在边缘可交换模型中用于交互过程中的节点级社区检测
Node-level community detection within edge exchangeable models for interaction processes
论文作者
论文摘要
科学家越来越有兴趣从大规模社交网络上产生的现代关系数据发现社区结构。尽管已经提出了许多用于学习社区结构的方法，但很少有人说明这些现代网络是由人口中互动过程引起的。我们介绍了块边缘可交换模型（BEEM），以研究具有潜在节点级社区结构的相互作用网络。块顶点组件模型（B-VCM）被推导为一个规范的示例。强调了以传统的以顶点为中心的方法的理论和实际优势。特别是，Beem可以在社区内进行稀疏学位结构和幂律学位分布。我们的理论分析界定了块分配的错误指定率，而支持模拟显示网络的属性可以恢复。得出了一种可计算障碍的吉布斯算法。我们使用TalkLife，一个大规模的在线点对点支持网络中的官方交互数据来证明提出的模型，并从使用标准算法的人士中对比学习的社区，包括光谱聚类和学位校正随机块模型。
Scientists are increasingly interested in discovering community structure from modern relational data arising on large-scale social networks. While many methods have been proposed for learning community structure, few account for the fact that these modern networks arise from processes of interactions in the population. We introduce block edge exchangeable models (BEEM) for the study of interaction networks with latent node-level community structure. The block vertex components model (B-VCM) is derived as a canonical example. Several theoretical and practical advantages over traditional vertex-centric approaches are highlighted. In particular, BEEMs allow for sparse degree structure and power-law degree distributions within communities. Our theoretical analysis bounds the misspecification rate of block assignments, while supporting simulations show the properties of the network can be recovered. A computationally tractable Gibbs algorithm is derived. We demonstrate the proposed model using post-comment interaction data from Talklife, a large-scale online peer-to-peer support network, and contrast the learned communities from those using standard algorithms including spectral clustering and degree-correct stochastic block models.
