Node Classification

This study investigates how community structure and homophily influence Graph Neural Networks (GNNs) in semi-supervised node classification tasks. By systematically modifying eight datasets and measuring GNN performance with and without these structural properties, we demonstrate their significant impact on classification accuracy and reveal insights about their interaction. We introduce an information-theoretic metric to quantify community-label correlation, providing practical guidelines for model selection based on graph structure. Our findings enhance understanding of GNN capabilities and improve model selection for semi-supervised node classification tasks.

This study examines how community structures in graphs affect Graph Neural Networks (GNNs) in node classification tasks. Through experiments on six datasets, researchers found that community structures significantly impact GNN performance. When nodes in a community mostly share the same label, disrupting the community structure dramatically reduces performance. Conversely, when labels don’t correlate with communities, graph structure becomes less relevant and simple feature-based models perform comparably. The research provides insights and guidelines for selecting appropriate models based on graph structure characteristics.