# A Graph Regularized Point Process Model For Event Propagation Sequence

arXiv: https://arxiv.org/abs/2211.11758 · HTML: https://mlsanity.com/paper/2211.11758/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Lecture Notes: Temporal Point Processes and the Conditional Intensity Function](https://mlsanity.com/paper/1806.00221/) (2018)
- [COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution](https://mlsanity.com/paper/1507.02293/) (2015)
- [Modeling Event Propagation via Graph Biased Temporal Point Process](https://mlsanity.com/paper/1908.01623/) (2019)
- [Self-Attentive Hawkes Processes](https://mlsanity.com/paper/1907.07561/) (2019)
- [The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process](https://mlsanity.com/paper/1612.09328/) (2016)
- [Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling](https://mlsanity.com/paper/1910.12469/) (2019)

## Built on by (most cited first, 17 of 17)

- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [Prompt-augmented Temporal Point Process for Streaming Event Sequence](https://mlsanity.com/paper/2310.04993/) (2023)
- [Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models](https://mlsanity.com/paper/2404.10209/) (2024)
- [Deep Optimal Timing Strategies for Time Series](https://mlsanity.com/paper/2310.05479/) (2023)
- [Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification](https://mlsanity.com/paper/2312.07338/) (2023)
- [Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference](https://mlsanity.com/paper/2309.02868/) (2023)
- [Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter](https://mlsanity.com/paper/2309.09443/) (2023)
- [GMTR: Graph Matching Transformers](https://mlsanity.com/paper/2311.08141/) (2023)
- [DarkShot: Lighting Dark Images with Low-Compute and High-Quality](https://mlsanity.com/paper/2312.16805/) (2023)
- [TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing](https://mlsanity.com/paper/2401.05431/) (2024)
- [TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks](https://mlsanity.com/paper/2401.05432/) (2024)
- [T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image](https://mlsanity.com/paper/2403.13663/) (2024)
- [Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithms](https://mlsanity.com/paper/2408.17101/) (2024)
- [Towards General Deepfake Detection with Dynamic Curriculum](https://mlsanity.com/paper/2410.11162/) (2024)
- [Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2410.13409/) (2024)
- [Mask6D: Masked Pose Priors For 6D Object Pose Estimation](https://mlsanity.com/paper/2507.06486/) (2025)
- [Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shifts](https://mlsanity.com/paper/2608.21554/) (2026)

## Abstract (arXiv)

Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. In this paper we aim at modeling latent dynamics of event propagation in graph, where the event sequence propagates in a directed weighted graph whose nodes represent event marks (e.g., event types). Most existing works have only considered encoding sequential event history into event representation and ignored the information from the latent graph structure. Besides they also suffer from poor model explainability, i.e., failing to uncover causal influence across a wide variety of nodes. To address these problems, we propose a Graph Regularized Point Process (GRPP) that can be decomposed into: 1) a graph propagation model that characterizes the event interactions across nodes with neighbors and inductively learns node representations; 2) a temporal attentive intensity model, whose excitation and time decay factors of past events on the current event are constructed via the contextualization of the node embedding. Moreover, by applying a graph regularization method, GRPP provides model interpretability by uncovering influence strengths between nodes. Numerical experiments on various datasets show that GRPP outperforms existing models on both the propagation time and node prediction by notable margins.