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

2026-02-25

AI Summary

This work addresses two critical challenges in social bot detection: severe class imbalance and graph topological noise induced by maliciously forged connections. To this end, the authors propose RABot, a novel framework that unifies reinforcement learning–driven dynamic edge filtering with neighborhood-aware linear interpolation oversampling within a multi-granularity graph augmentation paradigm. The former adaptively prunes spurious interactions to refine message passing, while the latter stabilizes decision boundaries within local subgraphs. Notably, RABot is orthogonal to graph neural network (GNN) architectures and can be seamlessly integrated as a plug-and-play module. Extensive experiments across three real-world datasets and four GNN backbones demonstrate that RABot significantly outperforms state-of-the-art methods with minimal computational overhead.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Safety and Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• class imbalance
• topological noise
• social bot detection
• graph neural networks
• imbalanced data

Innovation

• graph augmentation
• reinforcement learning
• class imbalance
• topological denoising
• social bot detection