Omkar2118/datasets huggingface dataset:
Yelp-Fraud
Full name: Multi-relational Graph Dataset for Yelp Spam Review Detection

Yelp-Fraud is a multi-relational graph dataset built upon the [Yelp spam review dataset](http://odds.cs.stonybrook.edu/yelpchi-dataset/), which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.

- **Dataset Statistics**

| # Nodes  |  %Fraud Nodes (Class=1) |
|-------|--------|
|  45,954 | 14.5   | 

| Relation  | # Edges |
|--------|--------|
  |   R-U-R    |  49,315 |
 |  R-T-R  |  573,616  |
|  R-S-R  |  3,402,743 |
 |  All |  3,846,979  |

- **Graph Construction**

The Yelp spam review dataset includes hotel and restaurant reviews filtered (spam) and recommended (legitimate) by Yelp. We conduct a spam review detection task on the Yelp-Fraud dataset which is a binary classification task. We take 32 handcrafted features from [SpEagle](http://shebuti.com/wp-content/uploads/2016/06/15-kdd-collectiveopinionspam.pdf) paper as the raw node features for Yelp-Fraud. Based on previous studies which show that opinion fraudsters have connections in user, product, review text, and time, we take reviews as nodes in the graph and design three relations: **1) R-U-R:** it connects reviews posted by the same user; **2) R-S-R:** it connects reviews under the same product with the same star rating (1-5 stars); **3) R-T-R:** it connects two reviews under the same product posted in the same month. 

To 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.

Introducing paper: Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Amazon-Fraud
Full name: Multi-relational Graph Dataset for Amazon Fraudulent Account Detection

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.

- **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](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.

To 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.

Introducing paper: Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters