Datasets › Amazon-Fraud
Amazon-Fraud (Multi-relational Graph Dataset for Amazon Fraudulent Account Detection)
Amazon-Fraud is a multi-relational graph dataset built upon the Amazon review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.
- Dataset Statistics
| # Nodes | %Fraud Nodes (Class=1) |
|---|---|
| 11,944 | 9.5 |
| Relation | # Edges |
|---|---|
| U-P-U | 175,608 |
| U-S-U | 3,566,479 |
| U-V-U | 1,036,737 |
| All | 4,398,392 |
- Graph Construction
The Amazon dataset includes product reviews under the Musical Instruments category. Similar to this paper, we label users with more than 80% helpful votes as benign entities and users with less than 20% helpful votes as fraudulent entities. we conduct a fraudulent user detection task on the Amazon-Fraud dataset, which is a binary classification task. We take 25 handcrafted features from this paper as the raw node features for Amazon-Fraud. We take users as nodes in the graph and design three relations: 1) U-P-U: it connects users reviewing at least one same product; 2) U-S-V: it connects users having at least one same star rating within one week; 3) U-V-U: it connects users with top 5% mutual review text similarities (measured by TF-IDF) among all users.
To download the dataset, please visit this Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.
Benchmarks archive 2025-07-28
All 3 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Fraud Detection | Amazon-Fraud | LEX-GNN AUC-ROC 97.91 | LEX-GNN: Label-Exploring Graph Neural Network for... | wdhyun/LEX-GNN | 6 | Compare |
| Node Classification | Amazon-Fraud | LEX-GNN AUC-ROC 97.91 | LEX-GNN: Label-Exploring Graph Neural Network for... | wdhyun/LEX-GNN | 6 | Compare |
| Graph Anomaly Detection | Amazon-Fraud | Grad AUC 97.89 | Grad: Guided Relation Diffusion Generation for Graph... | ai4risk/antifraud +2 | 1 | Compare |
Papers archive 2025-07-28
7 shown of 7 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 8. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection | 3 | 1 | 22 Apr 2025 | not harvested |
| Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation | 2 | 2 | 24 Dec 2024 | not harvested |
| LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection | 1 | 2 | 21 Oct 2024 | not harvested |
| RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection | 0 | 2 | 18 Jun 2021 | not harvested |
| Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection | 1 | 2 | 19 Apr 2021 | not harvested |
| Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks | 1 | 2 | 16 Apr 2021 | not harvested |
| Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters | 6 | 2 | 19 Aug 2020 | ran 1 of 17 samples (16 unverified) |
Dataset loaders archive 2025-07-28
2 loaders as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- Amazon-Fraud
1 variant name, as the archive lists them.
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