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Code for CIKM 2020 paper Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
| Date | Stars |
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| 2026-07-31 | 316 |
| 2026-08-05 | 316 |
| 2026-08-06 | 316 |
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# CARE-GNN
A PyTorch implementation for the [CIKM 2020](https://www.cikm2020.org/) paper below:
**Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters**.
[Yingtong Dou](http://ytongdou.com/), [Zhiwei Liu](https://sites.google.com/view/zhiwei-jim), [Li Sun](https://www.researchgate.net/profile/Li_Sun118), Yutong Deng, [Hao Peng](https://penghao-buaa.github.io/), [Philip S. Yu](https://www.cs.uic.edu/PSYu/).
\[[Paper](https://arxiv.org/pdf/2008.08692.pdf)\]\[[Toolbox](https://github.com/safe-graph/DGFraud)\]\[[DGL Example](https://github.com/dmlc/dgl/tree/master/examples/pytorch/caregnn)\]\[[Benchmark](https://paperswithcode.com/paper/enhancing-graph-neural-network-based-fraud)\]
## Bug Fixes and Update (06/2021)
### Similarity score
The feature and label similarity scores presented in Table 2 of the paper are incorrect. The updated equations for calculating two similarity scores are shown below:
<p align="center">
<br>
<a href="https://github.com/YingtongDou/CARE-GNN">
<img src="https://github.com/YingtongDou/CARE-GNN/blob/master/eq_simi.png" width="500"/>
</a>
<br>
<p>
The code for calculating the similarity scores is in [simi_comp.py](https://github.com/YingtongDou/CARE-GNN/blob/master/simi_comp.py).
The updated similarity scores for the two datasets are shown below. Note that we only compute the similarity scores for positive nodes to demonstrate the camouflage of fraudsters (positive nodes).
| YelpChi | rur | rtr | rsr | homo |
|-------|--------|--------|--------|--------|
| Avg. Feature Similarity | 0.991 | 0.988 | 0.988 | 0.988 |
| Avg. Label Similarity | 0.909 | 0.176 | 0.186 | 0.184 |
| Amazon | upu | usu | uvu | homo |
|-------|--------|--------|--------|--------|
| Avg. Feature Similarity | 0.711 | 0.687 | 0.697 | 0.687 |
| Avg. Label Similarity | 0.167 | 0.056 | 0.053 | 0.072 |
### Relation weight in Figure 3
According to this [issue](https://github.com/YingtongDou/CARE-GNN/issues/5), the weighted aggregation of CARE-Weight (a variant of CARE-GNN) has an error. After fixing it, the relation weight will not converge to the same value. Thus, the relation weight subfigure in Figure 3 and its associated conclusion are wrong.
### Extended version CARE-GNN
Please check out [RioGNN](https://github.com/safe-graph/RioGNN), a GNN model extended based on CARE-GNN with more reinforcement learning modules integrated. We are actively developing an efficient multi-layer version of CARE-GNN. Stay tuned.
## Overview
<p align="center">
<br>
<a href="https://github.com/YingtongDou/CARE-GNN">
<img src="https://github.com/YingtongDou/CARE-GNN/blob/master/model.png" width="900"/>
</a>
<br>
<p>
**CA**mouflage-**RE**sistant **G**raph **N**eural **N**etwork **(CARE-GNN)** is a GNN-based fraud detector based on a multi-relation graph equipped with three modules that enhance its performance against camouflaged fraudsters.
Three enhancement modules are:
- **A label-aware similarity measure** which measures the similarity scores between a center node and its neighboring nodes;
- **A similarity-aware neighbor selector** which leverages top-p sampling and reinforcement learning to select the optimal amount of neighbors under each relation;
- **A relation-aware neighbor aggregator** which directly aggregates information from different relations using the optimal neighbor selection thresholds as weights.
CARE-GNN has following advantages:
- **Adaptability.** CARE-GNN adaptively selects best neighbors
for aggregation given arbitrary multi-relation graph;
- **High-efficiency.** CARE-GNN has a high computational efficiency without attention and deep reinforcement learning;
- **Flexibility.** Many other neural modules and external knowledge can be plugged into the CARE-GNN;
We have integrated more than **eight** GNN-based fraud detectors as a TensorFlow [toolbox](https://github.com/safe-graph/DGFraud). Excerpt of 6,092 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:72c8e6c8d8b3423e, llm:Repository description and README: 'Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters'; topics include graphneuralnetwork, fraud-detection, deep-learning, reinforcement-learning, machine-learning; PyTorch implementation of a CIKM 2020 paper on GNN-based fraud detection.
matched fp:72c8e6c8d8b3423e, llm:Repository description and README: 'Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters'; topics include graphneuralnetwork, fraud-detection, deep-learning, reinforcement-learning, machine-learning; PyTorch implementation of a CIKM 2020 paper on GNN-based fraud detection.