Latent self-exciting point process model for spatial-temporal networks
- Author(s): Tita, George
- Brantingham, P Jeffrey
- Galstyan, Aram
- Cho, Yoon-Sik
- et al.
Published Web Locationhttps://doi.org/10.3934/dcdsb.2014.19.1335
We propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches that assume fully observable interactions, here we consider a scenario where certain interaction events lack information about participants. Instead, this information needs to be inferred from the available observations. We develop an efficient approximate algorithm based on variational expectation-maximization to infer unknown participants in an event given the location and the time of the event. We validate the model on synthetic as well as real-world data, and obtain very promising results on the identity-inference task. We also use our model to predict the timing and participants of future events, and demonstrate that it compares favorably with baseline approaches.
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