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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★ 2 cited
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…