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.

Positional encodings are fundamental to Transformers, yet explicit methods like RoPE can degrade under length extrapolation and may incur extra arithmetic and memory-access overhead. In this paper, we propose Scoped Position Encoding (ScoPE), a novel framework that reimagines structured sparsity as an intrinsic position encoding mechanism. Instead of relying on explicit arithmetic signals, ScoPE assigns exponentially scaled look-back scopes to attention heads. We theoretically demonstrate that this simple topological constraint transforms multi-head attention into a hierarchical processor, yielding an order awareness horizon that grows exponentially with depth up to the sequence length. Consequently, ScoPE is parameter-free and avoids relying on fragile positional arithmetic. Empirically, it significantly enhances efficiency by masking the majority of attention computations, offering a theoretical 8x reduction in attention FLOPs at long contexts. Extensive evaluations on LLaMA-3-8B architectures reveal that ScoPE achieves superior native length extrapolation and robust retrieval fidelity compared to RoPE, all while substantially reducing training and inference latency. The code is available at https://github.com/oncemoe/ScoPE.