mlsanityData to 7 Oct 2026

GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks

arXiv 2201.12741 ↗First posted 30 Jan 2022cs.LGcited by 14

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Abstract (arXiv)

Graph neural networks (GNNs) have been increasingly deployed in various applications that involve learning on non-Euclidean data. However, recent studies show that GNNs are vulnerable to graph adversarial attacks. Although there are several defense methods to improve GNN robustness by eliminating adversarial components, they may also impair the underlying clean graph structure that contributes to GNN training. In addition, few of those defense models can scale to large graphs due to their high computational complexity and memory usage. In this paper, we propose GARNET, a scalable spectral method to boost the adversarial robustness of GNN models. GARNET first leverages weighted spectral embedding to construct a base graph, which is not only resistant to adversarial attacks but also contains critical (clean) graph structure for GNN training. Next, GARNET further refines the base graph by pruning additional uncritical edges based on probabilistic graphical model. GARNET has been evaluated on various datasets, including a large graph with millions of nodes. Our extensive experiment results show that GARNET achieves adversarial accuracy improvement and runtime speedup over state-of-the-art GNN (defense) models by up to 13.27% and 14.7x, respectively.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 30 of its 48 references that are in the map. First posted on arXiv 30 Jan 2022.

Builds on

30 of 48 references.

References · 30

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Billion-scale similarity search with GPUscited by 2,477
  3. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  4. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  6. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  7. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
  8. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 592
  9. 2019Multi-scale Attributed Node Embeddingcited by 543
  10. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  11. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  12. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
18 more
  1. 2018Adversarial Attack on Graph Structured Datacited by 300
  2. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  3. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 199
  4. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
  5. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 166
  6. 2018Learning graphs from data: A signal representation perspectivecited by 119
  7. 2019Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Datacited by 104
  8. 2021Robustness of Graph Neural Networks at Scalecited by 100
  9. 2020DeepRobust: A PyTorch Library for Adversarial Attacks and Defensescited by 78
  10. 2016Hiding Individuals and Communities in a Social Networkcited by 77
  11. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  12. 2019GraphZoom: A multi-level spectral approach for accurate and scalable graph embeddingcited by 58
  13. 2017Analyzing the Robustness of Nearest Neighbors to Adversarial Examplescited by 36
  14. 2017Large Scale Graph Learning from Smooth Signalscited by 27
  15. 2014Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fieldscited by 19
  16. 2021SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluationcited by 16
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  18. 2021SGL: Spectral Graph Learning from Measurementscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 4; 2025: 1; 2026: 3.

14 papers.

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  1. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  2. 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approachcited by 24
  3. 2023GrOVe: Ownership Verification of Graph Neural Networks using Embeddingscited by 16
  4. 2023Robust Graph Neural Networks via Unbiased Aggregationcited by 8
  5. 2024SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Modelscited by 5
  6. 2024SAGMAN: Stability Analysis of Graph Neural Networks on the Manifoldscited by 4
  7. 2022ASGNN: Graph Neural Networks with Adaptive Structurecited by 1
  8. 2023Resilient Graph Neural Networks: A Coupled Dynamical Systems Approachcited by 1
  9. 2024Towards Robust Graph Structural Learning Beyond Homophily via Preserving Neighbor Similaritycited by 1
  10. 2024Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimizationcited by 1
  11. 2025Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledgecited by 0
  12. 2026COPYCOP: Ownership Verification for Graph Neural Networkscited by 0
2 more
  1. 2026Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluationcited by 0
  2. 2026FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defensecited by 0