  The presence of a large number of bots on social media leads to adverse
effects. Although Random forest algorithm is widely used in bot detection and
can significantly enhance the performance of weak classifiers, it cannot
utilize the interaction between accounts. 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. Specifically,
different subgraphs are constructed as different training sets through node
sampling, feature selection, and edge dropout. Then, GNN base classifiers are
trained using various subgraphs, and the remaining features are used for
training Fully Connected Netural Network (FCN). The outputs of GNN and FCN are
aligned in each branch. Finally, the outputs of all branches are aggregated to
produce the final result. Moreover, RF-GNN is compatible with various
widely-used GNNs for node classification. Extensive experimental results
demonstrate that the proposed method obtains better performance than other
state-of-the-art methods.
