6 papers
When Can You Correct Distribution Drift in Temporal Graph Generation? A Sharpening--Drift Tension and an Impossibility for Observation-Based Correction
Tianpeng Li, Xuan Guo, Wenjun Wang +2
Generative models of temporal graphs are trained on one stretch of an evolving network and deployed on the next, and they degrade badly in the gap. We show this degradation is deri…
Unsupervised Graph Representation Learning with Complementary View Alignment
Zengyi Wo, Shiyu Zhang, Qiyao Peng +2
Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data. Existin…
TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism
Hongjiang Chen, Pengfei Jiao, Ming Du +4
The growing interest in Temporal Graph Neural Networks (TGNNs) stems from their ability to model complex dynamics and deliver superior performance. However, TGNNs encounter fundame…
Towards OOD Generalization in Dynamic Graphs via Causal Invariant Learning
Xinxun Zhang, Pengfei Jiao, Mengzhou Gao +2
Although dynamic graph neural networks (DyGNNs) have demonstrated promising capabilities, most existing methods ignore out-of-distribution (OOD) shifts that commonly exist in dynam…
HGMP:Heterogeneous Graph Multi-Task Prompt Learning
Pengfei Jiao, Jialong Ni, Di Jin +4
The pre-training and fine-tuning methods have gained widespread attention in the field of heterogeneous graph neural networks due to their ability to leverage large amounts of unla…
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities
Pengfei Jiao, Hongjiang Chen, Xuan Guo +3
Temporal interaction graphs (TIGs), defined by sequences of timestamped interaction events, have become ubiquitous in real-world applications due to their capability to model compl…