AI RESEARCH
Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks
arXiv CS.LG
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ArXi:2604.10553v1 Announce Type: new Graph neural networks have nstrated excellent applicability to a wide range of domains, including social networks, biological systems, recommendation systems, and wireless communications. Yet a principled theoretical understanding of their generalization behavior remains limited, particularly for graph classification tasks where complex interactions between model parameters and graph structure play a crucial role.