PROXI: Challenging the GNNs for Link Prediction

TL;DR

This paper introduces PROXI, a proximity-based method for link prediction that outperforms many GNNs and enhances their performance, highlighting the need for improvements in current GNN models.

Contribution

The paper presents PROXI, a novel proximity-based approach for link prediction, and demonstrates its effectiveness in outperforming GNNs and boosting their performance.

Findings

Traditional ML models using proximity metrics perform competitively with GNNs.

Augmenting GNNs with PROXI significantly improves link prediction accuracy.

Current GNNs have room for improvement to match proximity-based methods.

Abstract

Over the past decade, Graph Neural Networks (GNNs) have transformed graph representation learning. In the widely adopted message-passing GNN framework, nodes refine their representations by aggregating information from neighboring nodes iteratively. While GNNs excel in various domains, recent theoretical studies have raised concerns about their capabilities. GNNs aim to address various graph-related tasks by utilizing such node representations, however, this one-size-fits-all approach proves suboptimal for diverse tasks. Motivated by these observations, we conduct empirical tests to compare the performance of current GNN models with more conventional and direct methods in link prediction tasks. Introducing our model, PROXI, which leverages proximity information of node pairs in both graph and attribute spaces, we find that standard machine learning (ML) models perform competitively, even outperforming cutting-edge GNN models when applied to these proximity metrics derived from node neighborhoods and attributes. This holds true across both homophilic and heterophilic networks, as well as small and large benchmark datasets, including those from the Open Graph Benchmark (OGB). Moreover, we show that augmenting traditional GNNs with PROXI significantly boosts their link prediction performance. Our empirical findings corroborate the previously mentioned theoretical observations and imply that there exists ample room for enhancement in current GNN models to reach their potential.