mlsanityData to 14 Oct 2026

How Robust Are Graph Neural Networks to Structural Noise?

arXiv 1912.10206 ↗First posted 21 Dec 2019cs.LG stat.MLcited by 15

Not on the map. Why?

Abstract (arXiv)

Graph neural networks (GNNs) are an emerging model for learning graph embeddings and making predictions on graph structured data. However, robustness of graph neural networks is not yet well-understood. In this work, we focus on node structural identity predictions, where a representative GNN model is able to achieve near-perfect accuracy. We also show that the same GNN model is not robust to addition of structural noise, through a controlled dataset and set of experiments. Finally, we show that under the right conditions, graph-augmented training is capable of significantly improving robustness to structural noise.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Dec 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 0; 2024: 5; 2025: 0; 2026: 1.

15 papers.

Sort:

  1. 2022G-Mixup: Graph Data Augmentation for Graph Classificationcited by 146
  2. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 110
  3. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  4. 2022Self-Supervised Graph Structure Refinement for Graph Neural Networkscited by 20
  5. 2021Adaptive Neural Message Passing for Inductive Learning on Hypergraphscited by 10
  6. 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunitiescited by 9
  7. 2021Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlookcited by 8
  8. 2024Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-trainingcited by 7
  9. 2021A Comparative Study on Robust Graph Neural Networks to Structural Noisescited by 4
  10. 2024Decoupled Subgraph Federated Learningcited by 3
  11. 2021NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Techniquecited by 2
  12. 2024Graph-based Active Learning for Entity Cluster Repaircited by 1
3 more
  1. 2022Multi-view graph structure learning using subspace merging on Grassmann manifoldcited by 0
  2. 2024Energy Guided smoothness to improve Robustness in Graph Classificationcited by 0
  3. 2026Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphscited by 0