mlsanityData to 14 Oct 2026

Modelling graph dynamics in fraud detection with "Attention"

arXiv 2204.10614 ↗First posted 22 Apr 2022cs.LG cs.AI cs.SIcited by 5

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Abstract (arXiv)

At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions. Despite the variety of different models for deep learning on graphs, few approaches have been proposed for dealing with graphs that are both heterogeneous and dynamic. In this paper, we propose DyHGN (Dynamic Heterogeneous Graph Neural Network) and its variants to capture both temporal and heterogeneous information. We first construct dynamic heterogeneous graphs from registration and transaction data from eBay. Then, we build models with diachronic entity embedding and heterogeneous graph transformer. We also use model explainability techniques to understand the behaviors of DyHGN-* models. Our findings reveal that modelling graph dynamics with heterogeneous inputs need to be conducted with "attention" depending on the data structure, distribution, and computation cost.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 22 Apr 2022.

Builds on

17 of 24 references.

References · 17

  1. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  2. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  3. 2019Heterogeneous Graph Attention Networkcited by 734
  4. 2020Heterogeneous Graph Transformercited by 582
  5. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  6. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  7. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
  8. 2019Diachronic Embedding for Temporal Knowledge Graph Completioncited by 104
  9. 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 96
  10. 2020APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embeddingcited by 78
  11. 2019An Attention-based Graph Neural Network for Heterogeneous Structural Learningcited by 71
  12. 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
5 more
  1. 2019Spam Review Detection with Graph Convolutional Networkscited by 57
  2. 2020xFraud: Explainable Fraud Transaction Detectioncited by 28
  3. 2020Uncovering Insurance Fraud Conspiracy with Network Learningcited by 9
  4. 2020Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networkscited by 6
  5. 2021Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecturecited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 2.

5 papers.

  1. 2022BRIGHT -- Graph Neural Networks in Real-Time Fraud Detectioncited by 15
  2. 2023STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphscited by 1
  3. 2023TimeTrail: Unveiling Financial Fraud Patterns through Temporal Correlation Analysiscited by 0
  4. 2025Explainable Fraud Detection with GNNExplainer and Shapley Valuescited by 0
  5. 2025A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platformscited by 0