# Modelling graph dynamics in fraud detection with "Attention"

arXiv: https://arxiv.org/abs/2204.10614 · HTML: https://mlsanity.com/paper/2204.10614/

## 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 (most cited first, 17 of 17)

- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks](https://mlsanity.com/paper/2112.14936/) (2021)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Diachronic Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1907.03143/) (2019)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding](https://mlsanity.com/paper/2011.11545/) (2020)
- [An Attention-based Graph Neural Network for Heterogeneous Structural Learning](https://mlsanity.com/paper/1912.10832/) (2019)
- [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://mlsanity.com/paper/1802.00910/) (2018)
- [Spam Review Detection with Graph Convolutional Networks](https://mlsanity.com/paper/1908.10679/) (2019)
- [xFraud: Explainable Fraud Transaction Detection](https://mlsanity.com/paper/2011.12193/) (2020)
- [Uncovering Insurance Fraud Conspiracy with Network Learning](https://mlsanity.com/paper/2002.12789/) (2020)
- [Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2012.10831/) (2020)
- [Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture](https://mlsanity.com/paper/2110.04559/) (2021)

## Built on by (most cited first, 5 of 5)

- [BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection](https://mlsanity.com/paper/2205.13084/) (2022)
- [STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs](https://mlsanity.com/paper/2309.15875/) (2023)
- [TimeTrail: Unveiling Financial Fraud Patterns through Temporal Correlation Analysis](https://mlsanity.com/paper/2308.14215/) (2023)
- [Explainable Fraud Detection with GNNExplainer and Shapley Values](https://mlsanity.com/paper/2509.12262/) (2025)
- [A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms](https://mlsanity.com/paper/2512.23777/) (2025)

## 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.