no code implementations • 5 Jul 2023 • Shuhao Shi, Kai Qiao, Zhengyan Wang, Jie Yang, Baojie Song, Jian Chen, Bin Yan
Recently, more and more GNN-based methods have been proposed for bot detection.
1 code implementation • 14 Apr 2023 • Shuhao Shi, Kai Qiao, Jie Yang, Baojie Song, Jian Chen, Bin Yan
This paper proposes a Random Forest boosted Graph Neural Network for social bot detection, called RF-GNN, which employs graph neural networks (GNNs) as the base classifiers to construct a random forest, effectively combining the advantages of ensemble learning and GNNs to improve the accuracy and robustness of the model.
1 code implementation • 14 Feb 2023 • Shuhao Shi, Kai Qiao, Jie Yang, Baojie Song, Jian Chen, Bin Yan
The proposed framework is evaluated using three real-world bot detection benchmark datasets, and it consistently exhibits superiority over the baselines.
1 code implementation • 3 Jan 2023 • Shuhao Shi, Kai Qiao, Jian Chen, Shuai Yang, Jie Yang, Baojie Song, Linyuan Wang, Bin Yan
However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research.
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