{"url":"/dataset/amazon-fraud","name":"Amazon-Fraud","full_name":"Multi-relational Graph Dataset for Amazon Fraudulent Account Detection","description_markdown":"Amazon-Fraud is a multi-relational graph dataset built upon the [Amazon review dataset](https://jmcauley.ucsd.edu/data/amazon/), which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.\r\n\r\n- **Dataset Statistics**\r\n\r\n| # Nodes  |  %Fraud Nodes  (Class=1)|\r\n|-------|--------|\r\n|  11,944 | 9.5   | \r\n\r\n| Relation  | # Edges |\r\n|--------|--------|\r\n  |   U-P-U    |  175,608 |\r\n |  U-S-U  |  3,566,479  |\r\n|  U-V-U  |  1,036,737 |\r\n |  All |  4,398,392  |\r\n\r\n- **Graph Construction**\r\n\r\nThe Amazon dataset includes product reviews under the Musical Instruments category. Similar to this [paper](https://arxiv.org/abs/2005.10150), 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](https://arxiv.org/abs/2005.10150) 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.\r\n\r\nTo download the dataset, please visit [this](https://github.com/YingtongDou/CARE-GNN) Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.","description_withheld":null,"homepage":"https://github.com/YingtongDou/CARE-GNN","introduced_date":"2020-08-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/enhancing-graph-neural-network-based-fraud","title":"Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters","first_author":"Yingtong Dou","url":null},"license":{"name":"Apache-2.0","url":"https://github.com/YingtongDou/CARE-GNN/blob/master/LICENSE"},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Node Classification","url":"/task/node-classification","datasets_with_task":"/datasets/task/node-classification"},{"name":"Fraud Detection","url":"/task/fraud-detection","datasets_with_task":"/datasets/task/fraud-detection"},{"name":"Graph Anomaly Detection","url":"/task/graph-anomaly-detection","datasets_with_task":"/datasets/task/graph-anomaly-detection"},{"name":"Graph Mining","url":"/task/graph-mining","datasets_with_task":"/datasets/task/graph-mining"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Amazon-Fraud"],"data_loaders":[{"repo":"https://github.com/dmlc/dgl","url":"https://docs.dgl.ai/api/python/dgl.data.html#fraud-dataset","frameworks":["pytorch"]},{"repo":"https://github.com/YingtongDou/CARE-GNN","url":"https://github.com/YingtongDou/CARE-GNN","frameworks":["pytorch"]}],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/fraud-detection-on-amazon-fraud","task":"Fraud Detection","dataset_variant":"Amazon-Fraud","rows":6,"metrics":["AUC-ROC","Averaged Precision","F1 Macro","G-mean"],"first_row_in_archive_order":{"model":"LEX-GNN","paper":"/paper/lex-gnn-label-exploring-graph-neural-network","metrics":{"AUC-ROC":"97.91","Averaged Precision":"92.18","F1 Macro":"93.48","G-mean":"92.03"},"code_links":[{"title":"wdhyun/LEX-GNN","url":"https://github.com/wdhyun/LEX-GNN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-amazon-fraud","task":"Node Classification","dataset_variant":"Amazon-Fraud","rows":6,"metrics":["AUC-ROC"],"first_row_in_archive_order":{"model":"LEX-GNN","paper":"/paper/lex-gnn-label-exploring-graph-neural-network","metrics":{"AUC-ROC":"97.91"},"code_links":[{"title":"wdhyun/LEX-GNN","url":"https://github.com/wdhyun/LEX-GNN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/graph-anomaly-detection-on-amazon-fraud","task":"Graph Anomaly Detection","dataset_variant":"Amazon-Fraud","rows":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"Grad","paper":"/paper/grad-guided-relation-diffusion-generation-for","metrics":{"AUC":"97.89"},"code_links":[{"title":"ai4risk/antifraud","url":"https://github.com/ai4risk/antifraud"},{"title":"Muyiiiii/WWW25-Grad","url":"https://github.com/Muyiiiii/WWW25-Grad"},{"title":"Muyiiiii-HF/WWW25-Grad","url":"https://huggingface.co/Muyiiiii-HF/WWW25-Grad"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/grad-guided-relation-diffusion-generation-for","title":"Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection","date":"2025-04-22","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/semi-supervised-credit-card-fraud-detection-1","title":"Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation","date":"2024-12-24","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/lex-gnn-label-exploring-graph-neural-network","title":"LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection","date":"2024-10-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/rlc-gnn-an-improved-deep-architecture-for","title":"RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection","date":"2021-06-18","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/pick-and-choose-a-gnn-based-imbalanced","title":"Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection","date":"2021-04-19","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/reinforced-neighborhood-selection-guided","title":"Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks","date":"2021-04-16","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/enhancing-graph-neural-network-based-fraud","title":"Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters","date":"2020-08-19","rows_on_this_dataset":2,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":1,"samples_unverified":16,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":17,"samples_ran":1,"samples_unverified":16,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}