RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling

• URL: http://arxiv.org/abs/2408.06665v1
• Date: Tue, 13 Aug 2024 06:34:56 GMT
• Title: RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling
• Authors: Shuqi He, Jun Zhuang, Ding Wang, Jun Song,
• Abstract summary: Graph Neural Networks (GNNs) have been widely applied in various practical scenarios, such as predicting user interests and detecting communities in social networks. Recent studies have shown that graph-structured networks often contain potential noise and attacks, in the form of topological perturbations and weight disturbances. We propose a novel method: Random Walk Negative Sampling Graph Conal Network (RW-NSGCN). Specifically, RW-NSGCN integrates the Random Walk with Restart (RWR) and PageRank algorithms for negative sampling and employs a Determinantal Point Process (DPP)-based GCN for convolution operations.
• Score: 10.124585385676376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Node classification using Graph Neural Networks (GNNs) has been widely applied in various practical scenarios, such as predicting user interests and detecting communities in social networks. However, recent studies have shown that graph-structured networks often contain potential noise and attacks, in the form of topological perturbations and weight disturbances, which can lead to decreased classification performance in GNNs. To improve the robustness of the model, we propose a novel method: Random Walk Negative Sampling Graph Convolutional Network (RW-NSGCN). Specifically, RW-NSGCN integrates the Random Walk with Restart (RWR) and PageRank (PGR) algorithms for negative sampling and employs a Determinantal Point Process (DPP)-based GCN for convolution operations. RWR leverages both global and local information to manage noise and local variations, while PGR assesses node importance to stabilize the topological structure. The DPP-based GCN ensures diversity among negative samples and aggregates their features to produce robust node embeddings, thereby improving classification performance. Experimental results demonstrate that the RW-NSGCN model effectively addresses network topology attacks and weight instability, increasing the accuracy of anomaly detection and overall stability. In terms of classification accuracy, RW-NSGCN significantly outperforms existing methods, showing greater resilience across various scenarios and effectively mitigating the impact of such vulnerabilities.

Related papers

• xAI-Drop: Don't Use What You Cannot Explain [23.33477769275026]
  Graph Neural Networks (GNNs) have emerged as the predominant paradigm for learning from graph-structured data. GNNs face challenges such as lack of generalization and poor interpretability. We introduce xAI-Drop, a novel topological-level dropping regularizer.
  arXiv  Detail & Related papers  (2024-07-29T14:53:45Z)
• DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment [57.62885438406724]
  Graph neural networks are recognized for their strong performance across various applications. BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. We propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning.
  arXiv  Detail & Related papers  (2024-06-04T07:24:51Z)
• HGAttack: Transferable Heterogeneous Graph Adversarial Attack [63.35560741500611]
  Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce. This paper introduces HGAttack, the first dedicated gray box evasion attack method for heterogeneous graphs.
  arXiv  Detail & Related papers  (2024-01-18T12:47:13Z)
• Attentional Graph Neural Networks for Robust Massive Network Localization [20.416879207269446]
  Graph neural networks (GNNs) have emerged as a prominent tool for classification tasks in machine learning. This paper integrates GNNs with attention mechanism to tackle a challenging nonlinear regression problem: network localization. We first introduce a novel network localization method based on graph convolutional network (GCN), which exhibits exceptional precision even under severe non-line-of-sight (NLOS) conditions.
  arXiv  Detail & Related papers  (2023-11-28T15:05:13Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification [24.91896527342631]
  Graph convolutional network (GCN) based models have shown impressive performance. Deep learning frameworks based on point estimation suffer from low generalization and inability to quantify the classification results uncertainty. In this paper, we propose a Bayesian layer with Bayesian idea as an insertion layer into point estimation based neural networks. A Generative Adversarial Network (GAN) is built to solve the sample imbalance problem of HSI dataset.
  arXiv  Detail & Related papers  (2022-11-14T12:56:56Z)
• GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections [20.18085461668842]
  Graph neural networks (GNNs) have found successful applications in various graph-related tasks. Recent studies have shown that many GNNs are vulnerable to adversarial attacks. In this paper, we focus on a realistic attack operation via injecting fake nodes.
  arXiv  Detail & Related papers  (2022-10-23T02:12:26Z)
• ASGNN: Graph Neural Networks with Adaptive Structure [41.83813812538167]
  We propose a novel interpretable message passing scheme with adaptive structure (ASMP) to defend against adversarial attacks on graph structure. ASMP is adaptive in the sense that the message passing process in different layers is able to be carried out over dynamically adjusted graphs.
  arXiv  Detail & Related papers  (2022-10-03T15:10:40Z)
• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [85.0332394224503]
  We study whether Graph Convolutional Networks (GCNs) can optimally integrate node features and topological structures in a complex graph with rich information. We propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN) Our experiments show that AM-GCN extracts the most correlated information from both node features and topological structures substantially.
  arXiv  Detail & Related papers  (2020-07-05T08:16:03Z)
• Bayesian Graph Neural Networks with Adaptive Connection Sampling [62.51689735630133]
  We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) The proposed framework not only alleviates over-smoothing and over-fitting tendencies of deep GNNs, but also enables learning with uncertainty in graph analytic tasks with GNNs.
  arXiv  Detail & Related papers  (2020-06-07T07:06:35Z)
• Stochastic Graph Neural Networks [123.39024384275054]
  Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning. Current GNN architectures assume ideal scenarios and ignore link fluctuations that occur due to environment, human factors, or external attacks. In these situations, the GNN fails to address its distributed task if the topological randomness is not considered accordingly.
  arXiv  Detail & Related papers  (2020-06-04T08:00:00Z)

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.