The Block Point Process Model for Continuous-time Event-based Dynamic Networks

Open access
Date
2019-05-13Type
- Conference Paper
Abstract
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
Permanent link
https://doi.org/10.3929/ethz-b-000347637Publication status
publishedExternal links
Book title
WWW '19 The World Wide Web ConferencePages / Article No.
Publisher
ACMEvent
Subject
block Hawkes model; event-based network; continuous-time net-work; timestamped network;; relational events; stochastic block model; Hawkes process; asymptotic independenceMore
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