GIF：一种基于影响函数的通用图遗忘策略
GIF: A General Graph Unlearning Strategy via Influence Function
AI总结：
本文提出了一种与模型无关的图遗忘方法GIF，通过在传统影响函数中引入受结构依赖影响的邻居损失项，高效准确地估计参数变化，在遗忘有效性、模型效用和效率上均表现优越。
摘要：
随着社会对隐私和安全的日益重视，图遗忘问题——即撤销特定数据对已训练GNN模型的影响，正受到越来越多的关注。然而，从机器遗忘到近期出现的图遗忘方法，现有努力要么诉诸重训练范式，要么执行近似擦除，这些近似方法未能考虑相连邻居之间的相互依赖性，或对GNN结构施加了限制，因此难以实现令人满意的性能与复杂度权衡。在本研究中，我们探索了专为图遗忘定制的影响函数，以提高图遗忘的有效性和效率。我们首先提出了针对节点、边和特征的多样化图遗忘任务的统一问题表述。然后，我们指出了传统影响函数在图遗忘中无能为力的症结所在，并设计了图影响函数（GIF），这是一种与模型无关的遗忘方法，能够高效且准确地估计参数在响应被删除数据中 $ε$-mass 扰动时的变化。其核心思想是在传统影响函数的目标中，补充一个因结构依赖而受影响邻居的额外损失项。对参数变化闭式解的进一步推导，有助于更好地理解遗忘机制。我们在四个具有代表性的GNN模型和三个基准数据集上进行了广泛实验，以证明GIF在遗忘有效性、模型效用和遗忘效率方面，对于多样化图遗忘任务的优越性。我们的实现可在 \url{https://github.com/wujcan/GIF-torch/} 获取。
英文摘要：
With the greater emphasis on privacy and security in our society, the problem of graph unlearning -- revoking the influence of specific data on the trained GNN model, is drawing increasing attention. However, ranging from machine unlearning to recently emerged graph unlearning methods, existing efforts either resort to retraining paradigm, or perform approximate erasure that fails to consider the inter-dependency between connected neighbors or imposes constraints on GNN structure, therefore hard to achieve satisfying performance-complexity trade-offs. In this work, we explore the influence function tailored for graph unlearning, so as to improve the unlearning efficacy and efficiency for graph unlearning. We first present a unified problem formulation of diverse graph unlearning tasks \wrt node, edge, and feature. Then, we recognize the crux to the inability of traditional influence function for graph unlearning, and devise Graph Influence Function (GIF), a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $ε$-mass perturbation in deleted data. The idea is to supplement the objective of the traditional influence function with an additional loss term of the influenced neighbors due to the structural dependency. Further deductions on the closed-form solution of parameter changes provide a better understanding of the unlearning mechanism. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify the superiority of GIF for diverse graph unlearning tasks in terms of unlearning efficacy, model utility, and unlearning efficiency. Our implementations are available at \url{https://github.com/wujcan/GIF-torch/}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
