Amazon-Fraud (Multi-relational Graph Dataset for Amazon Fraudulent Account Detection)

Introduced by Dou et al. in Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

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) for inquiry.


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