G-CutMix: A CutMix-based graph data augmentation method for bot detection in social networks - Yan Li; Shuhao Shi; Xiaofeng Guo; Chunhua Zhou; Qian Hu (PLoS ONE, 2025)
The CutMix technique is a sophisticated approach for augmenting data in order to train neural network-based image classifiers. Essentially, it involves cutting out a portion of a random image and pasting it into the same location as another image. However, because of the irregularity of graph data, CutMix cannot be directly applied to graph learning. Our paper introduces G-CutMix, a CutMix-based data augmentation approach that we designed specifically for bot detection in social media networks. G-CutMix involves conducting CutMix operations between the original graph and a shuffled graph, which precedes the graph convolution process. The outputs of the graph convolution are then strategically merged with the user representations from both the original and shuffled graphs. Our proposed G-CutMix not only leverages the power of graph convolutions but also introduces a layer of complexity that mimics real-world scenarios where bot behavior can be subtle and varied, making G-CutMix a formidable tool in the arsenal against bot detection. Our experiments confirm that our approach can consistently enhance the performance of bot detection across various GNN architectures, including Graph Convolutional Networks, GraphSAGE, and Graph Attention Networks.