SILO: Polynomial Graph Neural Networks: Theoretical Limits and Graph Noise Impact
Abstract: This talk examines the theoretical foundations of Graph Neural Networks (GNNs), focusing on polynomial GNNs (Poly-GNNs). We start with empirical evidence challenging the need for complex GNN architectures in semi-supervised node classification, showing simpler methods often perform comparably. We then analyze Poly-GNNs within a contextual stochastic block model, addressing …