Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation

• URL: http://arxiv.org/abs/2405.14407v2
• Date: Mon, 03 Feb 2025 00:55:19 GMT
• Title: Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation
• Authors: He Zhang, Bang Wu, Xiangwen Yang, Xingliang Yuan, Xiaoning Liu, Xun Yi,
• Abstract summary: We study the dynamic graph unlearning for the first time and propose an effective, efficient, general, and post-processing method to implement DGNN unlearning. Our method has the potential to handle future unlearning requests with significant performance gains.
• Score: 24.20087360102464
• License:
• Abstract: Dynamic graph neural networks (DGNNs) have emerged and been widely deployed in various web applications (e.g., Reddit) to serve users (e.g., personalized content delivery) due to their remarkable ability to learn from complex and dynamic user interaction data. Despite benefiting from high-quality services, users have raised privacy concerns, such as misuse of personal data (e.g., dynamic user-user/item interaction) for model training, requiring DGNNs to ``forget'' their data to meet AI governance laws (e.g., the ``right to be forgotten'' in GDPR). However, current static graph unlearning studies cannot \textit{unlearn dynamic graph elements} and exhibit limitations such as the model-specific design or reliance on pre-processing, which disenable their practicability in dynamic graph unlearning. To this end, we study the dynamic graph unlearning for the first time and propose an effective, efficient, general, and post-processing method to implement DGNN unlearning. Specifically, we first formulate dynamic graph unlearning in the context of continuous-time dynamic graphs, and then propose a method called Gradient Transformation that directly maps the unlearning request to the desired parameter update. Comprehensive evaluations on six real-world datasets and state-of-the-art DGNN backbones demonstrate its effectiveness (e.g., limited drop or obvious improvement in utility) and efficiency (e.g., 7.23$\times$ speed-up) advantages. Additionally, our method has the potential to handle future unlearning requests with significant performance gains (e.g., 32.59$\times$ speed-up).

Related papers

• Node-Time Conditional Prompt Learning In Dynamic Graphs [14.62182210205324]
  We propose DYGPROMPT, a novel pre-training and prompt learning framework for dynamic graph modeling. We recognize that node and time features mutually characterize each other, and propose dual condition-nets to model the evolving node-time patterns in downstream tasks.
  arXiv  Detail & Related papers  (2024-05-22T19:10:24Z)
• GraphPro: Graph Pre-training and Prompt Learning for Recommendation [18.962982290136935]
  GraphPro is a framework that incorporates parameter-efficient and dynamic graph pre-training with prompt learning. Our framework addresses the challenge of evolving user preferences by seamlessly integrating a temporal prompt mechanism and a graph-structural prompt learning mechanism.
  arXiv  Detail & Related papers  (2023-11-28T12:00:06Z)
• SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [131.04781590452308]
  We present SimTeG, a frustratingly Simple approach for Textual Graph learning. We first perform supervised parameter-efficient fine-tuning (PEFT) on a pre-trained LM on the downstream task. We then generate node embeddings using the last hidden states of finetuned LM.
  arXiv  Detail & Related papers  (2023-08-03T07:00:04Z)
• GIF: A General Graph Unlearning Strategy via Influence Function [63.52038638220563]
  Graph Influence Function (GIF) is a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $epsilon$-mass perturbation in deleted data. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify GIF's superiority in terms of unlearning efficacy, model utility, and unlearning efficiency.
  arXiv  Detail & Related papers  (2023-04-06T03:02:54Z)
• ROLAND: Graph Learning Framework for Dynamic Graphs [75.96510058864463]
  Graph Neural Networks (GNNs) have been successfully applied to many real-world static graphs. Existing dynamic GNNs do not incorporate state-of-the-art designs from static GNNs. We propose ROLAND, an effective graph representation learning framework for real-world dynamic graphs.
  arXiv  Detail & Related papers  (2022-08-15T14:51:47Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Efficient Dynamic Graph Representation Learning at Scale [66.62859857734104]
  We propose Efficient Dynamic Graph lEarning (EDGE), which selectively expresses certain temporal dependency via training loss to improve the parallelism in computations. We show that EDGE can scale to dynamic graphs with millions of nodes and hundreds of millions of temporal events and achieve new state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2021-12-14T22:24:53Z)
• Dynamic Graph Representation Learning via Graph Transformer Networks [41.570839291138114]
  We propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) DGT has spatial-temporal encoding to effectively learn graph topology and capture implicit links. We show that DGT presents superior performance compared with several state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-11-19T21:44:23Z)
• Learning to Evolve on Dynamic Graphs [5.1521870302904125]
  Learning to Evolve on Dynamic Graphs (LEDG) is a novel algorithm that jointly learns graph information and time information. LEDG is model-agnostic and can train any message passing based graph neural network (GNN) on dynamic graphs.
  arXiv  Detail & Related papers  (2021-11-13T04:09:30Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.