SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection

SAHG detects LLM-driven social bots by applying direction-dependent hyperbolic curvature and dual-channel feature fusion, achieving top accuracy and F1 across three benchmarks.

Hanning Lu, Yingguang Yang, Jinwei Su, Yang; Liu, Zhaoqian Yao, Yaoming Li, Taoran Liang, Ziyi Zhang, Ran Ran, Kefu Xu, Bin Chong

Published May 28, 2026arXiv ↗

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SAHG's sector-anisotropic hyperbolic geometry and dual-channel fusion top benchmarks, though critics question whether adaptive curvature genuinely outperforms fixed curvature or if independent channels merely delay neighborhood corruption without code or latency proof.

Abstract

LLM-driven social bots can generate fluent, human-like text, reducing the discriminative advantage of content-based detection alone. However, coordinated campaigns still leave relational patterns -- interactions, behavioral similarity, shared neighborhoods, community positions, and coordinated activity -- that graph-based methods can exploit. Existing graph detectors face two challenges when exploiting such evidence. First, Euclidean GNNs distort hierarchical and scale-free social graphs; while hyperbolic geometry addresses this volume-growth mismatch, fixed-curvature models still assign uniform geometric resolution to structural directions with different densities and separation needs. Second, relational evidence is not always reliable: sophisticated bots forge heterophilic connections with genuine users, causing neighborhood aggregation to mix bot and human signals and dilute account-level evidence. We propose SAHG (Sector-Anisotropic Hyperbolic Graph), addressing both challenges. SAHG learns a direction-dependent curvature field $γ(u)$ that adapts geometric resolution across structural directions, and uses sector prototypes to convert angular concentration and alignment into classifier-readable features. To prevent contaminated aggregation from overwhelming account-level evidence, SAHG encodes per-account features and graph-neighborhood representations in two independent SAH channels, fusing them only at the classifier. Experiments on Fox8-23, BotSim-24, and MGTAB show that SAHG achieves the highest accuracy and F1 on all three benchmarks, outperforming feature-based, graph-based, LLM-based, and isotropic hyperbolic baselines. Ablation and geometric analyses confirm the effectiveness of the anisotropic geometry and dual-channel design.