GSLB: The Graph Structure Learning Benchmark
Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Liang Wang Jeffrey Xu Yu
Graph Structure Learning (GSL) has recently garnered considerable attention
due to its ability to optimize both the parameters of Graph Neural Networks
(GNNs) and the computation graph structure simultaneously. Despite the
proliferation of GSL methods developed in recent years, there is no standard
experimental setting or fair comparison for performance evaluation, which
creates a great obstacle to understanding the progress in this field. To fill
this gap, we systematically analyze the performance of GSL in different
scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB)
curated from 20 diverse graph datasets and 16 distinct GSL algorithms.
Specifically, GSLB systematically investigates the characteristics of GSL in
terms of three dimensions: effectiveness, robustness, and complexity. We
comprehensively evaluate state-of-the-art GSL algorithms in node- and
graph-level tasks, and analyze their performance in robust learning and model
complexity. Further, to facilitate reproducible research, we have developed an
easy-to-use library for training, evaluating, and visualizing different GSL
methods. Empirical results of our extensive experiments demonstrate the ability
of GSL and reveal its potential benefits on various downstream tasks, offering
insights and opportunities for future research. The code of GSLB is available
at: https://github.com/GSL-Benchmark/GSLB.