Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. We also find that there is no significant correlation between the homophily of the learned structure and task performance, challenging the common belief. Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in this field. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.

翻译：图神经网络（GNN）凭借其有效整合图拓扑结构与节点属性的能力，已成为图表示学习的事实标准。然而，由于图形成过程的复杂性和偶然性，节点连接固有的次优性给有效建模带来了巨大挑战。为解决这一问题，图结构学习（GSL）——一类以数据为中心的学习方法——近年来受到广泛关注。其核心思想是联合优化图结构及其对应的GNN模型。尽管已有大量GSL方法被提出，但由于实验协议（包括数据集、数据处理技术和划分策略的差异）不一致，该领域的进展仍不清晰。本文首次提出面向GSL的综合基准OpenGSL，旨在填补这一空白。通过采用统一的数据处理和划分策略，OpenGSL在多个流行数据集上评估了多种前沿GSL方法，实现了公平比较。大量实验表明，现有GSL方法并非始终优于基础GNN模型。同时，我们发现学习结构的同质性参数与任务性能之间缺乏显著相关性，这一发现挑战了普遍认知。此外，尽管计算和空间开销较高，学习得到的图结构在不同GNN模型间展现出强大的泛化能力。我们期望开源库能够促进快速公平的评估，并推动该领域的创新研究。基准代码见https://github.com/OpenGSL/OpenGSL。