Modeling Events and Interactions through Temporal Processes -- A Survey

• URL: http://arxiv.org/abs/2303.06067v2
• Date: Fri, 21 Jul 2023 11:40:45 GMT
• Title: Modeling Events and Interactions through Temporal Processes -- A Survey
• Authors: Angelica Liguori, Luciano Caroprese, Marco Minici, Bruno Veloso, Francesco Spinnato, Mirco Nanni, Giuseppe Manco, Joao Gama
• Abstract summary: We revise the notion of event modeling and provide the foundations that characterize the literature on the topic. For each family, we systematically review existing approaches based on deep learning. We analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects.
• Score: 2.703218544805573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characterize the literature on the topic. We define an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically review the existing approaches based based on deep learning. Finally, we analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects.

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