mlsanityData to 14 Oct 2026

A Comparative Study on Robust Graph Neural Networks to Structural Noises

arXiv 2112.06070 ↗First posted 11 Dec 2021cs.LGcited by 4

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Abstract (arXiv)

Graph neural networks (GNNs) learn node representations by passing and aggregating messages between neighboring nodes. GNNs have been applied successfully in several application domains and achieved promising performance. However, GNNs could be vulnerable to structural noise because of the message passing mechanism where noise may be propagated through the entire graph. Although a series of robust GNNs have been proposed, they are evaluated with different structural noises, and it lacks a systematic comparison with consistent settings. In this work, we conduct a comprehensive and systematical comparative study on different types of robust GNNs under consistent structural noise settings. From the noise aspect, we design three different levels of structural noises, i.e., local, community, and global noises. From the model aspect, we select some representative models from sample-based, revision-based, and construction-based robust GNNs. Based on the empirical results, we provide some practical suggestions for robust GNNs selection.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 11 Dec 2021.

Builds on

13 of 23 references.

References · 13

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
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  6. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
  7. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  8. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 371
  9. 2019Learning Discrete Structures for Graph Neural Networkscited by 231
  10. 2019Graph-Revised Convolutional Networkcited by 59
  11. 2019Neural Message Passing for Multi-Label Classificationcited by 20
  12. 2019How Robust Are Graph Neural Networks to Structural Noise?cited by 15
1 more
  1. 2014Role Discovery in Networkscited by 12

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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 2.

4 papers.

  1. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  2. 2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
  3. 2022Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attackcited by 10
  4. 2024Graph-based Active Learning for Entity Cluster Repaircited by 1