mlsanityData to 14 Oct 2026

Heterogeneous Graph Neural Networks for Malicious Account Detection

arXiv 2002.12307 ↗First posted 27 Feb 2020cs.LG cs.CR stat.MLcited by 96

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Abstract (arXiv)

We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform. Our approach, inspired from a connected subgraph approach, adaptively learns discriminative embeddings from heterogeneous account-device graphs based on two fundamental weaknesses of attackers, i.e. device aggregation and activity aggregation. For the heterogeneous graph consists of various types of nodes, we propose an attention mechanism to learn the importance of different types of nodes, while using the sum operator for modeling the aggregation patterns of nodes in each type. Experiments show that our approaches consistently perform promising results compared with competitive methods over time.

Key facts. Cited by 96 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Feb 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 19; 2022: 17; 2023: 25; 2024: 16; 2025: 13; 2026: 6.

96 papers.

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  1. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  2. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  3. 2021Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Datacited by 87
  4. 2021A Review on Graph Neural Network Methods in Financial Applicationscited by 78
  5. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  6. 2022Simple and Efficient Heterogeneous Graph Neural Networkcited by 71
  7. 2021Accelerating Large Scale Real-Time GNN Inference using Channel Pruningcited by 41
  8. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  9. 2021Federated Graph Learning -- A Position Papercited by 35
  10. 2023Provably Powerful Graph Neural Networks for Directed Multigraphscited by 27
  11. 2024Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representationcited by 26
  12. 2024Graph Neural Networks for Financial Fraud Detection: A Reviewcited by 25
38 more
  1. 2021Heterogeneous Temporal Graph Neural Networkcited by 23
  2. 2024Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networkscited by 19
  3. 2023Homophily-oriented Heterogeneous Graph Rewiringcited by 18
  4. 2021A Biased Graph Neural Network Sampler with Near-Optimal Regretcited by 17
  5. 2022RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Searchcited by 17
  6. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  7. 2023Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysiscited by 14
  8. 2023Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detectioncited by 12
  9. 2023Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protectioncited by 12
  10. 2024SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learningcited by 12
  11. 2023Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Datacited by 11
  12. 2024Advanced Financial Fraud Detection Using GNN-CL Modelcited by 11
  13. 2023Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagationcited by 8
  14. 2022Efficient Graph Neural Network Inference at Large Scalecited by 7
  15. 2024CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networkscited by 7
  16. 2024VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detectioncited by 7
  17. 2022Graph Neural Networks: a bibliometrics overviewcited by 5
  18. 2022Improving Fraud Detection via Hierarchical Attention-based Graph Neural Networkcited by 5
  19. 2022Modelling graph dynamics in fraud detection with "Attention"cited by 5
  20. 2023Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approachcited by 5
  21. 2023GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detectioncited by 5
  22. 2023Graph Mining for Cybersecurity: A Surveycited by 5
  23. 2024Training-free Graph Neural Networks and the Power of Labels as Featurescited by 5
  24. 2021Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representationscited by 4
  25. 2022Graph Learning and Its Advancements on Large Language Models: A Holistic Surveycited by 4
  26. 2023InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphscited by 4
  27. 2023MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Networkcited by 4
  28. 2024Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learningcited by 4
  29. 2024KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Datacited by 4
  30. 2025PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modelingcited by 4
  31. 2021Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingcited by 3
  32. 2021Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networkscited by 3
  33. 2021Relational Graph Neural Networks for Fraud Detection in a Super-App environmentcited by 3
  34. 2021Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networkscited by 3
  35. 2021Deep Fraud Detection on Non-attributed Graphcited by 3
  36. 2022GNN-SL: Sequence Labeling Based on Nearest Examples via GNNcited by 3
  37. 2023Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platformscited by 3
  38. 2023Who Would be Interested in Services? An Entity Graph Learning System for User Targetingcited by 3

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