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Limin Li

2 accepted papers

2025

Enhancing Graph Invariant Learning from a Negative Inference Perspective

ICML 2025poster

The out-of-distribution (OOD) generalization challenge is a longstanding problem in graph learning. Through studying the fundamental cause of data distribution shift, i.e., the changes of environments, significant progress has been achieved in addressing this issue. However, we observe that existin…

Cited by 0SourcePDFScholar
2024

Improving Generalization of Dynamic Graph Learning via Environment Prompt

NeurIPS 2024poster

Out-of-distribution (OOD) generalization issue is a well-known challenge within deep learning tasks. In dynamic graphs, the change of temporal environments is regarded as the main cause of data distribution shift. While numerous OOD studies focusing on environment factors have achieved remarkable pe…

Cited by 1SourcePDFScholar