FRAUDAR: Bounding Graph Fraud in the Face of Camouflage

By Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos. Knowledge Discovery and Data Mining, 2016

393 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/2852982175beeb92e14127277cb158c4cb31f5c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Neil Zhenqiang Gong
2. Binghui Wang
3. Hao Fu
4. Chuan Zhou
5. Srijan Kumar
6. Hui Xiong
7. Quan Z. Sheng
8. Jia Wu

In Information Systems

1. Binghui Wang
2. Neil Zhenqiang Gong
3. Hao Fu
4. Srijan Kumar
5. Bryan Hooi
6. Hui Xiong
7. Jia Wu
8. Christos Faloutsos

In Sociology and Political Science

1. Neil Zhenqiang Gong
2. Binghui Wang
3. Hui Xiong
4. Christos Faloutsos
5. Srijan Kumar
6. Jure Leskovec

Citations in the corpus

• GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs (2017, methodology): “[24] proposed a dense-subgraph-based method to detect fraudulent accounts.”
• REV2: Fraudulent User Prediction in Rating Platforms (2018, background): “For example, a user with few but highly accurate ratings may be a honest user or a fraudster who is camouflaging itself [8].”
• A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, methodology): “We believe extensive effort should be placed on detecting these anomalies because, as yet, very few studies have looked at handling camouflaged anomalies in graphs [26], [27], [76].”