3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
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…