OpenGU: A Comprehensive Benchmark for Graph Unlearning

Bowen Fan, Yuming Ai, Xunkai Li, Zhilin Guo, Zhu Lei, Guang Zeng, Rong-Hua Li, Guoren Wang

Keywords: Graph Neural Network, Graph Unlearning

TL;DR: This paper presents the first Graph Unlearning benchmark, unifying diverse algorithms and offering key insights for future work.

Abstract:

Graph Machine Learning is essential for understanding and analyzing relational data. However, privacy-sensitive applications demand the ability to efficiently remove sensitive information from trained graph neural networks (GNNs), avoiding the unnecessary time and space overhead caused by retraining models from scratch.
To address this issue, Graph Unlearning (GU) has emerged as a critical solution to support dynamic graph updates while ensuring privacy compliance. Unlike machine unlearning in computer vision or other fields, GU faces unique difficulties due to the non-Euclidean nature of graph data and the recursive message-passing mechanism of GNNs. Additionally, the diversity of downstream tasks and the complexity of unlearning requests further amplify these challenges. Despite the proliferation of diverse GU strategies, the absence of a benchmark providing fair comparisons for GU, and the limited flexibility in combining downstream tasks and unlearning requests, have yielded inconsistencies in evaluations, hindering the development of this domain. To fill this gap, we present OpenGU, the first GU benchmark, where 16 SOTA GU algorithms and 37 multi-domain datasets are integrated, enabling various downstream tasks with 13 GNN backbones when responding to flexible unlearning requests. Through extensive experimentation, we have drawn $10$ crucial conclusions about existing GU methods, while also gaining valuable insights into their limitations, shedding light on potential avenues for future research. Our code is available at \href{https://github.com/bwfan-bit/OpenGU}{https://github.com/bwfan-bit/OpenGU}.

Code URL: https://github.com/bwfan-bit/OpenGU

Primary Area: Social and economic aspects of datasets and benchmarks in machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 955