Recent advances in Graph Neural Networks (GNNs) and Graph Transformers (GTs) have been driven by innovations in architectures and Positional Encodings (PEs), which are critical for augmenting node features and capturing graph topology. PEs are essential for GTs, where topological information would otherwise be lost without message-passing. However, PEs are often tested alongside novel architectures, making it difficult to isolate their effect on established models. To address this, we present a comprehensive benchmark of PEs in a unified framework that includes both message-passing GNNs and GTs. We also establish theoretical connections between MPNNs and GTs and introduce a sparsified GRIT attention mechanism to examine the influence of global connectivity. Our findings demonstrate that previously untested combinations of GNN architectures and PEs can outperform existing methods and offer a more comprehensive picture of the state-of-the-art. To support future research and experimentation in our framework, we make the code publicly available.

本研究解决了图神经网络(GNNs)和图变压器(GTs)中位置信息编码(PEs)影响的孤立性问题。通过建立一个统一的基准框架，我们系统地评估了PEs在GNN和GT中的应用，并引入了稀疏化的GRIT注意机制来检验全局连接的影响。研究发现，未曾测试的GNN架构与PEs的组合能够超越现有方法，为图神经网络研究提供了更全面的视角。

图神经网络和图变压器的位置信息编码基准测试