标题： 连续时间网络中依赖关系事件的多变量社区 Hawkes 模型 显示英文标题

标题： The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks

摘要： 随机块模型（SBM）是网络数据中最广泛使用的生成模型之一。 许多连续时间动态网络模型基于与SBM相同的假设：给定节点的块或社区隶属关系后，所有节点对之间的边或事件是条件独立的，这使得它们无法重现真实网络中常见的高阶模体（如三角形）。 我们提出了多变量社区Hawkes（MULCH）模型，这是一种极其灵活的基于社区的连续时间网络模型，通过使用结构化的多变量Hawkes过程在节点对之间引入依赖性。 我们使用谱聚类和基于似然的局部细化程序来拟合该模型。 我们发现，无论是预测任务还是生成任务，我们提出的MULCH模型都比现有模型准确得多。 显示英文摘要

摘要： 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.