Fraudulent promotion detection on GitHub using heterogeneous neural network

Zexin Ning; Peng Pu; Jiashen Lin

doi:10.1117/12.2667534

22 February 2023 Fraudulent promotion detection on GitHub using heterogeneous neural network

Zexin Ning, Peng Pu, Jiashen Lin

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 1258722 (2023) https://doi.org/10.1117/12.2667534
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

There are fraudulent promotion behaviors in GitHub, which promotes Stars and Forks for specific repositories. It is harmful to the environment of the open source community, while it is not effectively detected by GitHub yet. This paper applies a heterogeneous neural network to detect repositories that are suspected of fraudulent promotion behavior. A heterogenous mini-graph neural network with attention mechanism and hyper-graph generation is proposed to detect repositories with cheating behaviors. Attention mechanism can dynamically balance the weight of semantics in heterogeneous information networks. Hyper-graph generation method can solve the problem of poor connectivity caused by many small graphs in the dataset. The experimental result shows that the model can effectively detect this kind of cheating behavior.

Citation Download Citation

Zexin Ning, Peng Pu, and Jiashen Lin "Fraudulent promotion detection on GitHub using heterogeneous neural network", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 1258722 (22 February 2023); https://doi.org/10.1117/12.2667534

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