HW-GNN: Homophily-Aware Gaussian-Window Constraint Graph spectral network for Social Network Bot Detection

ACL ARR 2026 January Submission2352 Authors

Keywords: Social Bot Detection, Graph Neural Networks, Gaussian-Window Spectral Focusing, Homophily-Aware Mechanism

Abstract:

Social bots are increasingly polluting online platforms by spreading misinformation and engaging in coordinated manipulation, posing severe threats to cybersecurity. Graph Neural Networks (GNNs) have become mainstream for social bot detection due to their ability to integrate structural and attribute features, with spectral-based approaches demonstrating particular efficacy due to discriminative patterns in the spectral domain. However, current spectral GNN methods face two limitations: (1) their broad-spectrum fitting mechanisms degrade the focus on bot-specific spectral features, and (2) certain domain knowledge valuable for bot detection, e.g., low homophily correlates with high-frequency features, has not been fully incorporated into existing methods.

To address these challenges, we propose HWGNN, a novel homophily-aware graph spectral network with Gaussian window constraints. Our framework introduces two key innovations: (i) a Gaussian-window constrained spectral network that employs learnable Gaussian windows to highlight bot-related spectral features, and (ii) a homophily-aware adaptation mechanism that injects domain knowledge between homophily ratios and frequency features into the Gaussian window optimization process. Through extensive experimentation on multiple benchmark datasets, we demonstrate that HWGNN achieves state-of-the-art bot detection performance, outperforming existing methods with an average improvement of 4.6% in F1-score, while exhibiting strong plug-in compatibility with existing spectral GNNs.

Paper Type: Long

Research Area: Computational Social Science, Cultural Analytics, and NLP for Social Good

Research Area Keywords: misinformation detection and analysis, frame detection and analysis

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Reproduction study, Publicly available software and/or pre-trained models, Theory

Languages Studied: English

Submission Number: 2352