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.

翻译：过去十年间，图神经网络（GNNs）彻底改变了图表示学习领域。在广泛采用的消息传递GNN框架中，节点通过迭代聚合来自相邻节点的信息来优化其表示。尽管GNNs在多个领域表现出色，但近期的理论研究对其能力提出了质疑。GNNs旨在利用此类节点表示来解决各种图相关任务，然而这种“一刀切”的方法对于多样化的任务而言并非最优。基于这些观察，我们通过实证测试比较了当前GNN模型与更传统直接方法在链接预测任务中的性能。通过引入我们提出的PROXI模型——该模型综合利用节点对在图结构和属性空间中的邻近信息，我们发现标准机器学习（ML）模型在这些基于节点邻域与属性衍生的邻近度量上表现优异，甚至能够超越前沿的GNN模型。这一结论在同配性与异配性网络、小型与大型基准数据集（包括开放图基准OGB数据集）中均成立。此外，我们证明将PROXI与传统GNN结合能显著提升其链接预测性能。我们的实证结果验证了前述理论观察，并表明当前GNN模型在实现其潜力方面仍有巨大的提升空间。