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We consider the problem of analyzing timestamped relational events between aset 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 overtime to form network snapshots. In this paper, we introduce a block pointprocess model (BPPM) for continuous-time event-based dynamic networks. The BPPMis 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 agrowing number of nodes. We use this property to develop principled andefficient local search and variational inference procedures initialized byregularized spectral clustering. We fit BPPMs with exponential Hawkes processesto analyze several real network data sets, including a Facebook wall postnetwork with over 3,500 nodes and 130,000 events.

The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks