# Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks

arXiv: https://arxiv.org/abs/2012.10831 · HTML: https://mlsanity.com/paper/2012.10831/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)
- [BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection](https://mlsanity.com/paper/2205.13084/) (2022)
- [Modelling graph dynamics in fraud detection with "Attention"](https://mlsanity.com/paper/2204.10614/) (2022)
- [Fake News Detection with Heterogeneous Transformer](https://mlsanity.com/paper/2205.03100/) (2022)
- [Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture](https://mlsanity.com/paper/2110.04559/) (2021)
- [GraphFC: Customs Fraud Detection with Label Scarcity](https://mlsanity.com/paper/2305.11377/) (2023)

## Abstract (arXiv)

Massive account registration has raised concerns on risk management in e-commerce companies, especially when registration increases rapidly within a short time frame. To monitor these registrations constantly and minimize the potential loss they might incur, detecting massive registration and predicting their riskiness are necessary. In this paper, we propose a Dynamic Heterogeneous Graph Neural Network framework to capture suspicious massive registrations (DHGReg). We first construct a dynamic heterogeneous graph from the registration data, which is composed of a structural subgraph and a temporal subgraph. Then, we design an efficient architecture to predict suspicious/benign accounts. Our proposed model outperforms the baseline models and is computationally efficient in processing a dynamic heterogeneous graph constructed from a real-world dataset. In practice, the DHGReg framework would benefit the detection of suspicious registration behaviors at an early stage.