Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of applications. While the majority of GNN applications assume that a graph structure is given, some recent methods substantially expanded the applicability of GNNs by showing that they may be effective even when no graph structure is explicitly provided. The GNN parameters and a graph structure are jointly learned. Previous studies adopt different experimentation setups, making it difficult to compare their merits. In this paper, we propose a benchmarking strategy for graph structure learning using a unified framework. Our framework, called Unified Graph Structure Learning (UGSL), reformulates existing models into a single model. We implement a wide range of existing models in our framework and conduct extensive analyses of the effectiveness of different components in the framework. Our results provide a clear and concise understanding of the different methods in this area as well as their strengths and weaknesses. The benchmark code is available at https://github.com/google-research/google-research/tree/master/ugsl.

翻译：图神经网络（GNN）在广泛的应用领域展现出卓越性能。虽然大多数GNN应用假设图结构是给定的，但近期一些方法通过证明即使未显式提供图结构时GNN仍可能有效，显著扩展了其适用性。GNN参数与图结构被联合学习。以往的研究采用不同的实验配置，导致难以比较各自优劣。本文提出一种基于统一框架的图结构学习基准测试策略。我们的框架名为统一图结构学习（UGSL），将现有模型重构为单一模型。我们在该框架中实现了多种现有模型，并对框架中不同组件的有效性进行了广泛分析。实验结果为此领域的不同方法及其优缺点提供了清晰简洁的理解。基准测试代码见https://github.com/google-research/google-research/tree/master/ugsl。