Related papers: Enhancing Robustness of Graph Neural Networks through p-Laplacian

Enhancing Robustness of Graph Neural Networks through p-Laplacian

URL: http://arxiv.org/abs/2511.06143v1
Date: Sat, 08 Nov 2025 21:36:42 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 ap- plications, such as social network analysis, recommendation systems, drug discovery, and more.<n>This paper presents a computationally ef- ficient robustness framework, namely, pLAPGNN, based on weighted p-Laplacian for making GNNs robust.
Score: 2.984286239048672
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better pre- dictions. 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 en- tities, the need to design new techniques has arisen. For this graph data analysis has become an extraordinary tool for un- derstanding the data, which reveals more realistic and flexible modelling of complex relationships. Recently, Graph Neural Networks (GNNs) have shown great promise in various ap- plications, such as social network analysis, recommendation systems, drug discovery, and more. However, many adversar- ial 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 computa- tionally demanding and perform poorly when the intensity of attack increases. This paper presents a computationally ef- ficient 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

Neural Networks for Censored Expectile Regression Based on Data Augmentation [7.4480203741653535]
We propose a data augmentation based ERNNs algorithm, termed DAERNN, for modeling heterogeneous censored data.<n> Simulation studies and real data applications demonstrate that DAERNN outperforms existing censored ERNNs methods and achieves predictive performance comparable to models trained on fully observed data.
arXiv Detail & Related papers (2025-10-23T08:42:23Z)
Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction [16.068226406204253]
Link prediction in graph data uses various algorithms and Graph Nerual Network (GNN) models to predict potential relationships between graph nodes.<n>Recent research has highlighted the vulnerability of GNN models to adversarial attacks, such as poisoning and evasion attacks.<n>This article introduces an unweighted graph poisoning attack that leverages meta-learning with weighted scheme strategies to degrade the link prediction performance of GNNs.
arXiv Detail & Related papers (2025-04-08T23:36:29Z)
Enhancing Robustness of Graph Neural Networks through p-Laplacian [2.3942577670144423]
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.
arXiv Detail & Related papers (2024-09-27T18:51:05Z)
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks [50.87615167799367]
We certify Graph Neural Networks (GNNs) against poisoning attacks, including backdoors, targeting the node features of a given graph.<n>Our framework provides fundamental insights into the role of graph structure and its connectivity on the worst-case behavior of convolution-based and PageRank-based GNNs.
arXiv Detail & Related papers (2024-07-15T16:12:51Z)
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)
Uncertainty in Graph Neural Networks: A Survey [47.785948021510535]
Graph Neural Networks (GNNs) have been extensively used in various real-world applications.<n>However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions.<n>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)
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)
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)
Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
arXiv Detail & Related papers (2022-03-25T19:57:19Z)
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)
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.