spectral-gnn

This paper proposes HermNet, a spectral graph neural network model using Hermite polynomials, and analyzes its performance in graph filtering tasks through theoretical insights and experiments.

This paper introduces a nonlinear Laplacian operator for signed and directed graphs (NLSD) and a spectral GNN framework (NLSD-GNN) that achieves superior performance on node classification and link prediction by aligning message passing with edge direction.

This paper proposes a spectral graph reinforcement learning framework for outage detection and power restoration in self-healing smart grids, achieving near-optimal real-time performance on IEEE test systems.