Latent Self-Exciting Point Process Model for Spatial-Temporal Networks

@inproceedings{Cho2013LatentSP,
  title={Latent Self-Exciting Point Process Model for Spatial-Temporal Networks},
  author={Yoon-Sik Cho and Aram Galstyan and P. Jeffrey Brantingham and George E. Tita},
  year={2013}
}
Social network data is generally incomplete with missing information about nodes and their interactions. Here we propose a spatialtemporal latent point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches, we assume that interactions are not fully observable, and certain interaction events lack information about participants. Instead, this information needs to be inferred from the available observations. We… CONTINUE READING
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Self-exciting hurdle models for terrorist activity

M. D. Porter, G. White
The Annals of Applied Statistics, 6(1):106–124 • 2012
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