  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.
