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The presence of a large number of bots on social media leads to adverseeffects. Although Random forest algorithm is widely used in bot detection andcan significantly enhance the performance of weak classifiers, it cannotutilize the interaction between accounts. This paper proposes a Random Forestboosted Graph Neural Network for social bot detection, called RF-GNN, whichemploys graph neural networks (GNNs) as the base classifiers to construct arandom forest, effectively combining the advantages of ensemble learning andGNNs to improve the accuracy and robustness of the model. Specifically,different subgraphs are constructed as different training sets through nodesampling, feature selection, and edge dropout. Then, GNN base classifiers aretrained using various subgraphs, and the remaining features are used fortraining Fully Connected Netural Network (FCN). The outputs of GNN and FCN arealigned in each branch. Finally, the outputs of all branches are aggregated toproduce the final result. Moreover, RF-GNN is compatible with variouswidely-used GNNs for node classification. Extensive experimental resultsdemonstrate that the proposed method obtains better performance than otherstate-of-the-art methods.

RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection