Enhancing Robustness of Graph Neural Networks through p-Laplacian

• URL: http://arxiv.org/abs/2409.19096v1
• Date: Fri, 27 Sep 2024 18:51:05 GMT
• Title: Enhancing Robustness of Graph Neural Networks through p-Laplacian
• Authors: Anuj Kumar Sirohi, Subhanu Halder, Kabir Kumar, Sandeep Kumar,
• Abstract summary: Graph Neural Networks (GNNs) have shown great promise in various applications. adversarial attacks can happen over the data, whether during training (poisoning attack) or during testing (evasion attack) This paper presents a computationally efficient framework, namely, pLapGNN, based on weighted p-Laplacian for making GNNs robust.
• Score: 2.3942577670144423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a source of various insights, but with the increase in computational power and the need to understand deeper relationships between entities, the need to design new techniques has arisen. For this graph data analysis has become an extraordinary tool for understanding the data, which reveals more realistic and flexible modelling of complex relationships. Recently, Graph Neural Networks (GNNs) have shown great promise in various applications, such as social network analysis, recommendation systems, drug discovery, and more. However, many adversarial attacks can happen over the data, whether during training (poisoning attack) or during testing (evasion attack), which can adversely manipulate the desired outcome from the GNN model. Therefore, it is crucial to make the GNNs robust to such attacks. The existing robustness methods are computationally demanding and perform poorly when the intensity of attack increases. This paper presents a computationally efficient framework, namely, pLapGNN, based on weighted p-Laplacian for making GNNs robust. Empirical evaluation on real datasets establishes the efficacy and efficiency of the proposed method.

Related papers

• Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks [4.011211534057715]
  Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. In inductive GNNs, which allow for the processing of graph-structured data without relying on predefined graph structures, are becoming increasingly important in a wide range of applications. This paper identifies a new method of performing unsupervised model-stealing attacks against inductive GNNs.
  arXiv  Detail & Related papers  (2024-05-20T18:01:15Z)
• Uncertainty in Graph Neural Networks: A Survey [50.63474656037679]
  Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty.
  arXiv  Detail & Related papers  (2024-03-11T21:54:52Z)
• Rethinking Causal Relationships Learning in Graph Neural Networks [24.7962807148905]
  We introduce a lightweight and adaptable GNN module designed to strengthen GNNs' causal learning capabilities. We empirically validate the effectiveness of the proposed module.
  arXiv  Detail & Related papers  (2023-12-15T08:54:32Z)
• Addressing the Impact of Localized Training Data in Graph Neural Networks [0.0]
  Graph Neural Networks (GNNs) have achieved notable success in learning from graph-structured data. This article aims to assess the impact of training GNNs on localized subsets of the graph. We propose a regularization method to minimize distributional discrepancies between localized training data and graph inference.
  arXiv  Detail & Related papers  (2023-07-24T11:04:22Z)
• Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation [60.50994154879244]
  Recent studies show that Graph Neural Networks are vulnerable and easily fooled by small perturbations. In this work, we focus on the emerging but critical attack, namely, Graph Injection Attack. We propose a general defense framework CHAGNN against GIA through cooperative homophilous augmentation of graph data and model.
  arXiv  Detail & Related papers  (2022-11-15T11:44:31Z)
• EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks [51.42338058718487]
  Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs. We propose EvenNet, a spectral GNN corresponding to an even-polynomial graph filter.
  arXiv  Detail & Related papers  (2022-05-27T10:48:14Z)
• Generalizing Graph Neural Networks on Out-Of-Distribution Graphs [51.33152272781324]
  Graph Neural Networks (GNNs) are proposed without considering the distribution shifts between training and testing graphs. In such a setting, GNNs tend to exploit subtle statistical correlations existing in the training set for predictions, even though it is a spurious correlation. We propose a general causal representation framework, called StableGNN, to eliminate the impact of spurious correlations.
  arXiv  Detail & Related papers  (2021-11-20T18:57:18Z)
• Unveiling the potential of Graph Neural Networks for robust Intrusion Detection [2.21481607673149]
  We propose a novel Graph Neural Network (GNN) model to learn flow patterns of attacks structured as graphs. Our model is able to maintain the same level of accuracy as in previous experiments, while state-of-the-art ML techniques degrade up to 50% their accuracy (F1-score) under adversarial attacks.
  arXiv  Detail & Related papers  (2021-07-30T16:56:39Z)
• How effective are Graph Neural Networks in Fraud Detection for Network Data? [0.0]
  Graph-based Neural Networks (GNNs) are recent models created for learning representations of nodes (and graphs) Financial fraud stands out for its socioeconomic relevance and for presenting particular challenges, such as the extreme imbalance between the positive (fraud) and negative (legitimate transactions) classes. We conduct experiments to evaluate existing techniques for detecting network fraud, considering the two previous challenges.
  arXiv  Detail & Related papers  (2021-05-30T15:17:13Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)

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.