The Block Point Process Model for Continuous-time Event-based Dynamic Networks
- Conference Paper
Rights / licenseCreative Commons Attribution 4.0 International
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. Show more
Book titleWWW '19 The World Wide Web Conference
Pages / Article No.
Subjectblock Hawkes model; event-based network; continuous-time net-work; timestamped network;; relational events; stochastic block model; Hawkes process; asymptotic independence
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