ASPECT: Node-Level Adaptive Spectral Fusion for Graph Contrastive Learning

Spectral graph contrastive learning constructs complementary low- and high-frequency views, but existing methods typically combine them using graph-level or node-agnostic fusion rules. We show that such global fusion can incur irreducible regret when nodes have heterogeneous spectral preferences, motivating node-wise spectral fusion. We propose ASPECT, which learns a node-wise policy to fuse low- and high-frequency views and regularizes this policy using channel-wise contrastive evidence. We further introduce ASPECT-S, a stability-aware extension that incorporates channel-wise sensitivity estimated from generated graph-structure and feature perturbations, with a Rayleigh-based bias for perturbation search. Experiments on homophilic and heterophilic graphs show that ASPECT consistently improves clean representation quality over competitive spectral and graph contrastive baselines, while ASPECT-S further improves performance under joint structural and feature perturbations.