# Group Network Hawkes Process

arXiv: https://arxiv.org/abs/2002.08521 · HTML: https://mlsanity.com/paper/2002.08521/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Mamba Hawkes Process](https://mlsanity.com/paper/2407.05302/) (2024)
- [On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes](https://mlsanity.com/paper/2406.00630/) (2024)
- [A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior](https://mlsanity.com/paper/2411.04397/) (2024)
- [Deep spatio-temporal point processes: Advances and new directions](https://mlsanity.com/paper/2504.06364/) (2025)
- [Amortized Network Intervention to Steer the Excitatory Point Processes](https://mlsanity.com/paper/2310.04159/) (2023)
- [Learning under Commission and Omission Event Outliers](https://mlsanity.com/paper/2501.13599/) (2025)
- [GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs](https://mlsanity.com/paper/2607.14733/) (2026)
- [Poisson Autoregression on a Large Network with a Stochastic Block Model Structure](https://mlsanity.com/paper/2610.05365/) (2026)

## Abstract (arXiv)

In this work, we study the event occurrences of individuals interacting in a network. To characterize the dynamic interactions among the individuals, we propose a group network Hawkes process (GNHP) model whose network structure is observed and fixed. In particular, we introduce a latent group structure among individuals to account for the heterogeneous user-specific characteristics. A maximum likelihood approach is proposed to simultaneously cluster individuals in the network and estimate model parameters. A fast EM algorithm is subsequently developed by utilizing the branching representation of the proposed GNHP model. Theoretical properties of the resulting estimators of group memberships and model parameters are investigated under both settings when the number of latent groups G is over-specified or correctly specified. A data-driven criterion that can consistently identify the true G under mild conditions is derived. Extensive simulation studies and an application to a data set collected from Sina Weibo are used to illustrate the effectiveness of the proposed methodology.