mlsanityData to 7 Oct 2026

ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks

arXiv 2009.14738 ↗First posted 30 Sep 2020cs.LG stat.MLcited by 24

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Abstract (arXiv)

Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches have difficulties with three major issues: sparsity and nonlinearity capturing, residual modeling, and network smoothing. We propose Residual Graph Convolutional Network (ResGCN), an attention-based deep residual modeling approach that can tackle these issues: modeling the attributed networks with GCN allows to capture the sparsity and nonlinearity; utilizing a deep neural network allows to directly learn residual from the input, and a residual-based attention mechanism reduces the adverse effect from anomalous nodes and prevents over-smoothing. Extensive experiments on several real-world attributed networks demonstrate the effectiveness of ResGCN in detecting anomalies.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2020.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 5; 2024: 6; 2025: 1; 2026: 1.

24 papers.

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  1. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  2. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 46
  3. 2023Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detectioncited by 43
  4. 2024Deep Graph Anomaly Detection: A Survey and New Perspectivescited by 40
  5. 2021Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networkscited by 31
  6. 2021Pseudo-Riemannian Graph Convolutional Networkscited by 30
  7. 2022Graph Anomaly Detection with Graph Neural Networks: Current Status and Challengescited by 23
  8. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  9. 2024Topology-aware Reinforcement Feature Space Reconstruction for Graph Datacited by 11
  10. 2022Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognitioncited by 8
  11. 2022ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awarenesscited by 8
  12. 2024Community-Invariant Graph Contrastive Learningcited by 4
12 more
  1. 2021On Generalization of Graph Autoencoders with Adversarial Trainingcited by 3
  2. 2022A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Networkcited by 3
  3. 2023Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networkscited by 3
  4. 2021GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphscited by 2
  5. 2023Efficient Network Representation for GNN-based Intrusion Detectioncited by 2
  6. 2024UMGAD: Unsupervised Multiplex Graph Anomaly Detectioncited by 2
  7. 2022GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networkscited by 1
  8. 2023Label-based Graph Augmentation with Metapath for Graph Anomaly Detectioncited by 1
  9. 2022A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networkscited by 0
  10. 2023A Graph Encoder-Decoder Network for Unsupervised Anomaly Detectioncited by 0
  11. 2025Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomescited by 0
  12. 2026Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detectioncited by 0