Graph Unlearning with Efficient Partial Retraining

• URL: http://arxiv.org/abs/2403.07353v2
• Date: Wed, 13 Mar 2024 04:43:23 GMT
• Title: Graph Unlearning with Efficient Partial Retraining
• Authors: Jiahao Zhang, Lin Wang, Shijie Wang, Wenqi Fan
• Abstract summary: Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications. GNNs may be trained on undesirable graph data, which can degrade their performance and reliability. We propose GraphRevoker, a novel graph unlearning framework that better maintains the model utility of unlearnable GNNs.
• Score: 28.433619085748447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications. However, GNNs may be trained on undesirable graph data, which can degrade their performance and reliability. To enable trained GNNs to efficiently unlearn unwanted data, a desirable solution is retraining-based graph unlearning, which partitions the training graph into subgraphs and trains sub-models on them, allowing fast unlearning through partial retraining. However, the graph partition process causes information loss in the training graph, resulting in the low model utility of sub-GNN models. In this paper, we propose GraphRevoker, a novel graph unlearning framework that better maintains the model utility of unlearnable GNNs. Specifically, we preserve the graph property with graph property-aware sharding and effectively aggregate the sub-GNN models for prediction with graph contrastive sub-model aggregation. We conduct extensive experiments to demonstrate the superiority of our proposed approach.

Related papers

• Stealing Training Graphs from Graph Neural Networks [54.52392250297907]
  Graph Neural Networks (GNNs) have shown promising results in modeling graphs in various tasks. As neural networks can memorize the training samples, the model parameters of GNNs have a high risk of leaking private training data. We investigate a novel problem of stealing graphs from trained GNNs.
  arXiv  Detail & Related papers  (2024-11-17T23:15:36Z)
• SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks [5.008597638379227]
  Graph neural networks (GNNs) have become the de facto model of choice for graph classification. We propose a regularization strategy that can be applied to any GNN to improve its generalization capabilities without requiring access to the test data. Our regularization is based on the idea of simulating a shift in the size of the training graphs using coarsening techniques.
  arXiv  Detail & Related papers  (2022-07-16T09:50:45Z)
• 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)
• OOD-GNN: Out-of-Distribution Generalized Graph Neural Network [73.67049248445277]
  Graph neural networks (GNNs) have achieved impressive performance when testing and training graph data come from identical distribution. Existing GNNs lack out-of-distribution generalization abilities so that their performance substantially degrades when there exist distribution shifts between testing and training graph data. We propose an out-of-distribution generalized graph neural network (OOD-GNN) for achieving satisfactory performance on unseen testing graphs that have different distributions with training graphs.
  arXiv  Detail & Related papers  (2021-12-07T16:29:10Z)
• Increase and Conquer: Training Graph Neural Networks on Growing Graphs [116.03137405192356]
  We consider the problem of learning a graphon neural network (WNN) by training GNNs on graphs sampled Bernoulli from the graphon. Inspired by these results, we propose an algorithm to learn GNNs on large-scale graphs that, starting from a moderate number of nodes, successively increases the size of the graph during training.
  arXiv  Detail & Related papers  (2021-06-07T15:05:59Z)
• Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping [116.26579152942162]
  Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data. Despite their success, GNNs suffer from sub-optimal generalization performance given limited training data. This paper proposes Topology Adaptive Edge Dropping to improve generalization performance and learn robust GNN models.
  arXiv  Detail & Related papers  (2021-06-05T13:20:36Z)
• GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy [5.466414428765544]
  We present a new distributed graph learning system GraphTheta. It supports multiple training strategies and enables efficient and scalable learning on big graphs. This work represents the largest edge-attributed GNN learning task conducted on a billion-scale network in the literature.
  arXiv  Detail & Related papers  (2021-04-21T14:51:33Z)
• XGNN: Towards Model-Level Explanations of Graph Neural Networks [113.51160387804484]
  Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information. GNNs are mostly treated as black-boxes and lack human intelligible explanations. We propose a novel approach, known as XGNN, to interpret GNNs at the model-level.
  arXiv  Detail & Related papers  (2020-06-03T23:52:43Z)

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.