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Lian Shen

2 accepted papers

2025

LOHA: Direct Graph Spectral Contrastive Learning Between Low-Pass and High-Pass Views

AAAI 2025technical

Spectral Graph Neural Networks effectively handle graphs with different homophily levels, with low-pass filter mining feature smoothness and high-pass filter capturing differences. When these distinct filters could naturally form two opposite views for self-supervised learning, the commonalities bet…

Cited by 0SourcePDFScholar
2025

RETAIN: Reliable Topology Augmentation for both Heterophilic and Homophilic Graphs

ICASSP 2025accepted

Current graph topology augmentation methods are mostly static and heavily rely on the assumption of homophily, where connected nodes are presumed to share the same labels by default. Due to the complexity of real-world graphs, the underlying assumption is often disrupted, thus performance declines,…

Cited by 0SourceScholar