Supervised Attention Using Homophily in Graph Neural Networks

TL;DR

This paper introduces a supervised attention mechanism in graph neural networks that leverages homophily to improve node classification accuracy by encouraging higher attention scores among nodes of the same class.

Contribution

The authors propose a novel technique to incorporate class-aware supervision into graph attention models, enhancing their ability to distinguish node classes.

Findings

Improved node classification accuracy on multiple datasets.

Higher attention scores between nodes of the same class.

Enhanced separation of node representations.

Abstract

Graph neural networks have become the standard approach for dealing with learning problems on graphs. Among the different variants of graph neural networks, graph attention networks (GATs) have been applied with great success to different tasks. In the GAT model, each node assigns an importance score to its neighbors using an attention mechanism. However, similar to other graph neural networks, GATs aggregate messages from nodes that belong to different classes, and therefore produce node representations that are not well separated with respect to the different classes, which might hurt their performance. In this work, to alleviate this problem, we propose a new technique that can be incorporated into any graph attention model to encourage higher attention scores between nodes that share the same class label. We evaluate the proposed method on several node classification datasets demonstrating increased performance over standard baseline models.