  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.
