建模动态网络中社区的演化与交互，能捕捉合并、分裂等复杂行为。
Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure
- 用泊松-伽马边分割建模边的归属，通过非负成员关系分配节点到社区。
- 在真实数据集上，链接预测和社区检测性能优于现有先进模型。
- 适合研究社交网络、生物网络等随时间演变的复杂系统。
我们提出一种新型动态网络模型，用于捕捉时序网络中的演化潜在社区结构。通过泊松-伽马边分割模型分解顶点间的观测动态边，利用非负的顶点-社区隶属关系将每个顶点分配至一个或多个潜在社区。具体地，采用分层转移核建模这些潜在社区在观测时序网络中的交互。对潜在社区的转移结构施加分层图先验，使其能够建模社区随时间的演化与交互过程。由此，该动态网络可实现社区的合并、分裂及相互作用，提供对复杂网络动态的全面理解。在多个真实世界网络数据集上的实验表明，所提模型不仅能有效发现可解释的潜在结构，且在链接预测与社区检测任务上超越其他先进动态网络模型。
原文摘要 · Abstract (English)
We propose a novel dynamic network model to capture evolving latent communities within temporal networks. To achieve this, we decompose each observed dynamic edge between vertices using a Poisson-gamma edge partition model, assigning each vertex to one or more latent communities through \emph{nonnegative} vertex-community memberships. Specifically, hierarchical transition kernels are employed to model the interactions between these latent communities in the observed temporal network. A hierarchical graph prior is placed on the transition structure of the latent communities, allowing us to model how they evolve and interact over time. Consequently, our dynamic network enables the inferred community structure to merge, split, and interact with one another, providing a comprehensive understanding of complex network dynamics. Experiments on various real-world network datasets demonstrate that the proposed model not only effectively uncovers interpretable latent structures but also surpasses other state-of-the art dynamic network models in the tasks of link prediction and community detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。