/sybildetection

Structure-based Sybil/Fake account/Spam detection in social networks

Primary LanguageC++MIT LicenseMIT

Sybildetection

Structure-based Sybil/fake account detection in online social networks

The codes implement the SybilBelief, SybilRank, SybilSCAR, and GANG algorithms.

When using it in your research work, you should cite the following papers:

Neil Zhenqiang Gong, Mario Frank, and Prateek Mittal. "SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection". In IEEE Transactions on Information Forensics and Security (TIFS), 9(6), 2014.

Binghui Wang, Le Zhang, and Neil Zhenqiang Gong. "SybilSCAR: Sybil Detection in Online Social Networks via Local Rule based Propagation". In IEEE International Conference on Computer Communications (INFOCOM), 2017.

Binghui Wang, Jinyuan, Jia, Le Zhang, and Neil Zhenqiang Gong. "Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation". IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (TNSE), 2019.

Binghui Wang, Neil Zhenqiang Gong, and Hao Fu, “GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs”, IEEE International Conference on Data Mining (ICDM), 2017.

For any question, please contact Binghui Wang (binghui.wang@duke.edu).