On Local Aggregation in Heterophilic Graphs

Understand

Many recent works have studied the performance of Graph Neural Networks (GNNs) in the context of graph homophily - a label-dependent measure of connectivity.

• Traditional GNNs generate node embeddings by aggregating information from a node's neighbors in the graph.
• Recent results in node classification tasks show that this local aggregation approach performs poorly in graphs with low homophily (heterophilic graphs).
• Several mechanisms have been proposed to improve the accuracy of GNNs on such graphs by increasing the aggregation range of a GNN layer, either through multi-hop aggregation, or through long-range aggregation from distant nodes.