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Cignn: Community-Induced Graph Neural Networks, Shi Yin Hong

Computer Science and Computer Engineering Undergraduate Honors Theses

Message-passing graph neural networks (MPGNNs) are known to have limitations in their representational power. Recent work proposes subgraph graph neural network (subgraph GNN) models to address these limitations by upgrading the local node representations of MPGNNs to respective subgraph representations. However, existing subgraph GNN models have limited interpretability in capturing inherent local structural dynamics across diverse graph structures. In this work, we present a novel subgraph GNNs framework, termed Community-Induced Graph Neural Network (CiGNN). The key idea of CiGNN is to endow an intuitive interpretability basis for subgraph GNNs by capturing the dynamics of inherent structural community topology in subgraph …