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Proposes DCQ-GNN, a spectral GNN that uses a compact bank of adaptive convex-concave quadratic filters to improve spectral selectivity without high-order polynomials, achieving competitive results on both homophilic and heterophilic graphs.
This paper investigates whether aggregate structural invariants, specifically spectral bounds, can accelerate continuous subgraph matching (CSM) over dynamic graphs. It characterizes limitations of lazy spectral maintenance, shows exact maintenance is affordable when selective, and demonstrates pruning power of up to 51% in benchmarks.