mlsanityData to 14 Oct 2026

Robust Graph Representation Learning for Local Corruption Recovery

arXiv 2202.04936 ↗First posted 10 Feb 2022cs.LGcited by 6

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Abstract (arXiv)

The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a globally smoothed graph embedding, we believe the rarely observed anomalies are as well harmful to an accurate prediction. This work establishes a graph learning scheme that automatically detects (locally) corrupted feature attributes and recovers robust embedding for prediction tasks. The detection operation leverages a graph autoencoder, which does not make any assumptions about the distribution of the local corruptions. It pinpoints the positions of the anomalous node attributes in an unbiased mask matrix, where robust estimations are recovered with sparsity promoting regularizer. The optimizer approaches a new embedding that is sparse in the framelet domain and conditionally close to input observations. Extensive experiments are provided to validate our proposed model can recover a robust graph representation from black-box poisoning and achieve excellent performance.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 51 of its 71 references that are in the map. First posted on arXiv 10 Feb 2022.

Builds on

51 of 71 references.

References · 51

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38 more
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50 most cited of 51 All 51 (JSON)

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Cited by year

Citing papers per year: 2023: 2; 2024: 4.

6 papers.

  1. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  2. 2024Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimationcited by 11
  3. 2023Framelet Message Passingcited by 3
  4. 2024You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learningcited by 3
  5. 2024ROGPL: Robust Open-Set Graph Learning via Region-Based Prototype Learningcited by 2
  6. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0