  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.
