论文标题
连续时间网络中依赖关系事件的多元社区鹰队模型
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
论文作者
论文摘要
随机块模型（SBM）是用于网络数据最广泛使用的生成模型之一。考虑到与SBM相同的假设，许多连续的动态网络模型构建了：鉴于块或社区成员身份，所有对节点之间的边缘或事件在条件上是独立的，这阻止了它们再现高阶基序，例如在真实网络中通常观察到的三角形。我们提出了多元社区霍克斯（Mulch）模型，这是一种非常灵活的基于社区的模型，用于连续时间网络，使用结构化的多元霍克斯过程在节点对之间引入依赖性。我们使用基于光谱聚类和基于可能性的本地改进程序拟合模型。我们发现，我们所提出的覆盖模型比在预测和生成任务的现有模型要准确得多。
The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or events between all pairs of nodes are conditionally independent given the block or community memberships, which prevents them from reproducing higher-order motifs such as triangles that are commonly observed in real networks. We propose the multivariate community Hawkes (MULCH) model, an extremely flexible community-based model for continuous-time networks that introduces dependence between node pairs using structured multivariate Hawkes processes. We fit the model using a spectral clustering and likelihood-based local refinement procedure. We find that our proposed MULCH model is far more accurate than existing models both for predictive and generative tasks.
