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

TL;DR

RABot introduces a reinforcement-guided graph augmentation framework that improves social bot detection by addressing class imbalance and topological noise, enhancing GNN performance across multiple benchmarks.

Contribution

It presents a novel multi-granularity augmentation approach combining oversampling and reinforcement learning-based edge filtering for robust social bot detection.

Findings

Outperforms state-of-the-art methods on three benchmarks

Effective across four different GNN architectures

Seamless integration with existing GNN pipelines

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.