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Networks and temporal point processes serve as fundamental building blocksfor modeling complex dynamic relational data in various domains. We propose thelatent space Hawkes (LSH) model, a novel generative model for continuous-timenetworks of relational events, using a latent space representation for nodes.We model relational events between nodes using mutually exciting Hawkesprocesses with baseline intensities dependent upon the distances between thenodes in the latent space and sender and receiver specific effects. Wedemonstrate that our proposed LSH model can replicate many features observed inreal temporal networks including reciprocity and transitivity, while alsoachieving superior prediction accuracy and providing more interpretable fitsthan existing models.

A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks