ASPECT: Node-Level Adaptive Spectral Fusion for Graph Contrastive Learning
Spectral graph contrastive learning often constructs low- and high-frequency views to capture complementary graph signals, but these views are commonly combined by graph-level or node-agnostic fusion rules. We show that graph-level fusion can incur irreducible regret on mixed graphs with separated node-wise spectral preferences. Motivated by this result, we propose ASPECT, a spectral graph contrastive learning method that adaptively fuses low- and high-frequency views at the node level. ASPECT learns a node-wise spectral policy and regularizes it using channel-wise contrastive evidence, enabling different nodes to use different spectral mixtures. We further introduce ASPECT-S, an optional...
| Authors | Zhuolong Li, Boxue Yang, Haopeng Chen |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 6 May 2026 |
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