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

TL;DR

RF-GNN combines graph neural networks with random forest ensemble techniques to significantly improve social bot detection accuracy and robustness by leveraging subgraph sampling and multi-branch aggregation.

Contribution

This paper introduces RF-GNN, a novel ensemble model that integrates GNNs with random forest principles for enhanced social bot detection.

Findings

RF-GNN outperforms existing state-of-the-art methods in accuracy.

The method improves robustness against diverse social bot behaviors.

RF-GNN is compatible with various GNN architectures.

Abstract

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.