Homophily Principle

The homophily principle, stating that connected nodes in a network tend to share similar characteristics, is a cornerstone assumption in many graph neural network (GNN) models. Current research focuses on addressing the limitations of this assumption, particularly in heterophilic graphs where connected nodes exhibit dissimilar properties, by developing new GNN architectures that incorporate higher-order information, adaptive propagation mechanisms, and improved homophily metrics. These advancements aim to enhance the performance and robustness of GNNs across diverse applications, improving accuracy in tasks such as node classification and anomaly detection in scenarios where the homophily assumption is violated.