Homophily Metric

Homophily metrics quantify the tendency of connected nodes in a graph to share similar characteristics, a crucial concept for understanding the performance of graph neural networks (GNNs). Recent research focuses on improving the accuracy and applicability of these metrics, particularly addressing limitations in handling heterophily (where connected nodes are dissimilar), and developing more comprehensive metrics that consider label, structural, and feature similarities. This work is significant because accurate homophily assessment is vital for designing effective GNNs and interpreting their performance across diverse datasets, impacting various applications relying on graph-structured data analysis.