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Activity Number:
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124
- Algorithms for Threat Detection
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Type:
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Topic-Contributed
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Date/Time:
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Monday, August 9, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Statistics in Defense and National Security
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Abstract #317192
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Title:
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CHIP: A Hawkes Process Model for Continuous-Time Networks of Relational Events
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Author(s):
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Subhadeep Paul* and Kevin Xu and Makan Arastuie
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Companies:
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The Ohio State University and University of Toledo and University of Toledo
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Keywords:
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Continuous-time networks;
Relational events;
Spatiotemporal modeling;
Point process;
stochastic block model;
network analysis
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Abstract:
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In many application settings involving networks, such as messages between users of an online social network or transactions between traders in financial markets, the observed data consist of timestamped relational events, which form a continuous-time network. We propose the Community Hawkes Independent Pairs (CHIP) generative model for such networks. We show that applying spectral clustering to an aggregated adjacency matrix constructed from the CHIP model provides consistent community detection for a growing number of nodes and time duration. We also develop consistent and computationally efficient estimators for the model parameters. We demonstrate that our proposed CHIP model and estimation procedure scales to large networks with tens of thousands of nodes and provides superior fits compared to existing continuous-time network models on several real networks.
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Authors who are presenting talks have a * after their name.
