The Block Point Process Model for Continuous-time Event-based Dynamic Networks
The Block Point Process Model for Continuous-time Event-based Dynamic Networks
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DOI：
10.1145/3308558.3313633
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Ruthwik R. Junuthula;Maysam Haghdan;Kevin S. Xu;V. Devabhaktuni
中科院分区：
文献类型：
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作者：
Ruthwik R. Junuthula;Maysam Haghdan;Kevin S. Xu;V. Devabhaktuni
我们考虑的问题，分析一组实体之间的时间戳的关系事件，如用户之间的在线社交网络的消息。这些数据通常使用静态或离散时间网络模型进行分析，这些模型通过随时间聚合事件以形成网络快照来丢弃大量信息。在本文中，我们介绍了一个块点过程模型（BPPM）的连续时间基于事件的动态网络。BPPM的灵感来自于著名的静态网络的随机块模型（SBM）。我们表明，由BPPM生成的网络遵循SBM的节点数量不断增加的限制。我们使用这个属性开发的原则和有效的本地搜索和变分推理程序初始化正则谱聚类。我们将BPPM与指数Hawkes过程拟合，以分析几个真实的网络数据集，包括一个拥有3,500多个节点和130,000个事件的Facebook墙帖网络。
We consider the problem of analyzing timestamped relational events between a set of entities, such as messages between users of an on-line social network. Such data are often analyzed using static or discrete-time network models, which discard a significant amount of information by aggregating events over time to form network snapshots. In this paper, we introduce a block point process model (BPPM) for continuous-time event-based dynamic networks. The BPPM is inspired by the well-known stochastic block model (SBM) for static networks. We show that networks generated by the BPPM follow an SBM in the limit of a growing number of nodes. We use this property to develop principled and efficient local search and variational inference procedures initialized by regularized spectral clustering. We fit BPPMs with exponential Hawkes processes to analyze several real network data sets, including a Facebook wall post network with over 3,500 nodes and 130,000 events.