UGSL: A Unified Framework for Benchmarking Graph Structure Learning
This work addresses the need for standardized evaluation in graph structure learning, which is incremental as it consolidates existing methods rather than introducing new ones.
The paper tackles the problem of inconsistent experimental setups in graph structure learning by proposing UGSL, a unified benchmarking framework that reformulates existing models into a single model, enabling extensive analysis and clear comparison of their strengths and weaknesses.
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.