G-CutMix: A CutMix-based graph data augmentation method for bot detection in social networks.

ABSTRACT: 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-Cu

SUBMITTER: Li Y 

PROVIDER: S-EPMC12469246 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature