The presence of a large number of bots in Online Social Networks (OSN) leads to undesirable social effects. Graph neural networks (GNNs) are effective in detecting bots as they utilize user interactions. However, class-imbalanced issues can affect bot detection performance. To address this, we propose an over-sampling strategy for GNNs (OS-GNN) that generates samples for the minority class without edge synthesis. First, node features are mapped to a feature space through neighborhood aggregation. Then, we generate samples for the minority class in the feature space. Finally, the augmented features are used to train the classifiers. This framework is general and can be easily extended into different GNN architectures. The proposed framework is evaluated using three real-world bot detection benchmark datasets, and it consistently exhibits superiority over the baselines.

翻译：在线社交网络（OSN）中存在大量机器人会导致不良社会效应。图神经网络（GNN）通过利用用户交互能够有效检测机器人。然而，类别不平衡问题可能影响机器人检测性能。为解决这一问题，我们提出了一种面向GNN的过采样策略（OS-GNN），在不进行边合成的情况下为少数类生成样本。首先，通过邻域聚合将节点特征映射到特征空间；随后，在该特征空间中为少数类生成样本；最后，使用增强后的特征训练分类器。该框架具有通用性，可轻松扩展至不同的GNN架构。我们使用三个真实机器人检测基准数据集对提出框架进行验证，结果表明其持续优于基线方法。