# TREND: TempoRal Event and Node Dynamics for Graph Representation Learning

arXiv: https://arxiv.org/abs/2203.14303 · HTML: https://mlsanity.com/paper/2203.14303/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2022.

## Built on by (most cited first, 30 of 50)

- [Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs](https://mlsanity.com/paper/2311.15317/) (2023)
- [Deep Temporal Graph Clustering](https://mlsanity.com/paper/2305.10738/) (2023)
- [Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment](https://mlsanity.com/paper/2302.07491/) (2023)
- [A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning](https://mlsanity.com/paper/2208.14073/) (2022)
- [Graph Information Bottleneck for Remote Sensing Segmentation](https://mlsanity.com/paper/2312.02545/) (2023)
- [A survey of dynamic graph neural networks](https://mlsanity.com/paper/2404.18211/) (2024)
- [Spatio-Temporal Graph Neural Networks: A Survey](https://mlsanity.com/paper/2301.10569/) (2023)
- [TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics](https://mlsanity.com/paper/2502.02975/) (2025)
- [Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs](https://mlsanity.com/paper/2211.08568/) (2022)
- [Towards Open Temporal Graph Neural Networks](https://mlsanity.com/paper/2303.15015/) (2023)
- [Parameter-free Dynamic Graph Embedding for Link Prediction](https://mlsanity.com/paper/2210.08189/) (2022)
- [Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation](https://mlsanity.com/paper/2304.11528/) (2023)
- [Node-Time Conditional Prompt Learning In Dynamic Graphs](https://mlsanity.com/paper/2405.13937/) (2024)
- [Robust Knowledge Adaptation for Dynamic Graph Neural Networks](https://mlsanity.com/paper/2207.10839/) (2022)
- [TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification](https://mlsanity.com/paper/2309.11845/) (2023)
- [Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding](https://mlsanity.com/paper/2407.16959/) (2024)
- [Retrieval Augmented Generation for Dynamic Graph Modeling](https://mlsanity.com/paper/2408.14523/) (2024)
- [A Survey of Link Prediction in Temporal Networks](https://mlsanity.com/paper/2502.21185/) (2025)
- [Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models](https://mlsanity.com/paper/2503.14411/) (2025)
- [Temporal Graph Representation Learning with Adaptive Augmentation Contrastive](https://mlsanity.com/paper/2311.03897/) (2023)
- [Future Link Prediction Without Memory or Aggregation](https://mlsanity.com/paper/2505.19408/) (2025)
- [Temporal Link Prediction Using Graph Embedding Dynamics](https://mlsanity.com/paper/2401.07516/) (2024)
- [A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2505.04461/) (2025)
- [arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering](https://mlsanity.com/paper/2306.04962/) (2023)
- [Input Snapshots Fusion for Scalable Discrete-Time Dynamic Graph Neural Networks](https://mlsanity.com/paper/2405.06975/) (2024)
- [Temporal Graph Pattern Machine](https://mlsanity.com/paper/2601.22454/) (2026)
- [TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagation](https://mlsanity.com/paper/2210.01171/) (2022)
- [Exploring Time Granularity on Temporal Graphs for Dynamic Link Prediction in Real-world Networks](https://mlsanity.com/paper/2311.12255/) (2023)
- [Link-aware link prediction over temporal graph by pattern recognition](https://mlsanity.com/paper/2402.07199/) (2024)
- [Temporal Generalization Estimation in Evolving Graphs](https://mlsanity.com/paper/2404.04969/) (2024)

## Abstract (arXiv)

Temporal graph representation learning has drawn significant attention for the prevalence of temporal graphs in the real world. However, most existing works resort to taking discrete snapshots of the temporal graph, or are not inductive to deal with new nodes, or do not model the exciting effects which is the ability of events to influence the occurrence of another event. In this work, We propose TREND, a novel framework for temporal graph representation learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes process-based graph neural network (GNN). TREND presents a few major advantages: (1) it is inductive due to its GNN architecture; (2) it captures the exciting effects between events by the adoption of the Hawkes process; (3) as our main novelty, it captures the individual and collective characteristics of events by integrating both event and node dynamics, driving a more precise modeling of the temporal process. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed model.