RABot: Reinforcement-Guided Graph Augmentation for Imbalanced and Noisy Social Bot Detection

TL;DR

The Reinforcement-guided graph Augmentation social Bot detector (RABot), a multi-granularity graph-augmentation framework that addresses both issues in a unified manner and can be seamlessly integrated into existing GNN pipelines to boost performance with minimal overhead is proposed.

Abstract

Social bot detection is pivotal for safeguarding the integrity of online information ecosystems. Although recent graph neural network (GNN) solutions achieve strong results, they remain hindered by two practical challenges: (i) severe class imbalance arising from the high cost of generating bots, and (ii) topological noise introduced by bots that skillfully mimic human behavior and forge deceptive links. We propose the Reinforcement-guided graph Augmentation social Bot detector (RABot), a multi-granularity graph-augmentation framework that addresses both issues in a unified manner. RABot employs a neighborhood-aware oversampling strategy that linearly interpolates minority-class embeddings within local subgraphs, thereby stabilizing the decision boundary under low-resource regimes. Concurrently, a reinforcement-learning-driven edge-filtering module combines similarity-based edge features with adaptive threshold optimization to excise spurious interactions during message passing, yielding a cleaner topology. Extensive experiments on three real-world benchmarks and four GNN backbones demonstrate that RABot consistently surpasses state-of-the-art baselines. In addition, since its augmentation and filtering modules are orthogonal to the underlying architecture, RABot can be seamlessly integrated into existing GNN pipelines to boost performance with minimal overhead.

Figures (5)

• Figure 1: Unreliable aggregation in social networks (left). Performance variation after removing all unreliable edges on Twibot-20 (right).
• Figure 2: Overall structure of RABot model, which consists of four modules: user information representation module, feature augmentation module, reinforcement edge filtering module and GNN classification module.
• Figure 3: Performance of RABot with a RGCN backbone versus random edge removal of different drop rates on the Cresci-15, Twibot-20, and MGTAB datasets.
• Figure 4: Data-efficiency study. The model is trained on random subsamples of the original training data ($10\%$-$100\%$) from Twibot-20 and MGTAB, and evaluates on the full test sets.
• Figure 5: Sensitivity of RABot with a GAT backbone to the loss weights $\lambda_s$ and $\lambda_e$ on the Twibot-20 and MGTAB.