Adversarial attacks on graphs have posed a major threat to the robustness of
graph machine learning (GML) models. Naturally, there is an ever-escalating
arms race between attackers and defenders. However, the strategies behind both
sides are often not fairly compared under the same and realistic conditions. To
bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal
of providing a scalable, unified, modular, and reproducible evaluation for the
adversarial robustness of GML models. GRB standardizes the process of attacks
and defenses by 1) developing scalable and diverse datasets, 2) modularizing
the attack and defense implementations, and 3) unifying the evaluation protocol
in refined scenarios. By leveraging the GRB pipeline, the end-users can focus
on the development of robust GML models with automated data processing and
experimental evaluations. To support open and reproducible research on graph
adversarial learning, GRB also hosts public leaderboards across different
scenarios. As a starting point, we conduct extensive experiments to benchmark
baseline techniques. GRB is open-source and welcomes contributions from the
community. Datasets, codes, leaderboards are available at
this https URL

本文介绍 Graph Robustness Benchmark (GRB) 以及如何利用其提供的标准化攻击和防御过程、统一评估协议、公共排行榜评估和数据处理来发展更为健壮的图机器学习模型。