Jul 2026· EAI Endorsed Transactions on AI and Robotics· 0 citations· 27 references
TL;DR
Results indicate that decoupling behavioural signatures from adversarial social positioning is valuable for modern bot detection, and that combining accuracy with interpretable evidence supports deployment in real-world moderation settings.
Abstract
Social bots threaten the integrity of online ecosystems by engaging in coordinated opinion manipulation. While Graph Neural Networks (GNNs) have become a dominant paradigm for bot detection, modern camouflaged bots strategically follow benign users to evade detection, creating structural heterophily that degrades the performance of standard homophilic GNN aggregators; moreover, many existing detectors offer limited forensic explainability. To address these challenges jointly, we propose XHBot (eXplainable Heterophily-aware Bot detector), a framework that is robust to heterophilic relation camouflage while providing transparent, multi-level forensic evidence for platform moderation. XHBot couples three components: Spectral-Guided Topology Refinement (SGTR), which down-weights camouflage edges by their contribution to the graph’s high-frequency (Dirichlet) energy before aggregation; Tri-Channel Heterophily-Aware Aggregation (THCA), which separates homophilic, heterophilic, and self-identity signals; and Contrastive Prototype Disentanglement (CPD), which decouples behavioural signatures from social positioning. Evaluated on TwiBot-20, TwiBot-22, and Cresci-2017 under a unified protocol, XHBot reaches an F1 score of 0.9474 on TwiBot-20, improving over a competitive suite of recent baselines (including RGT, NeighborSense, and HW-GNN) by 9.64%. Its Hierarchical Forensic Explanation (HFE) module extracts both instance-level subgraphs and community-level diagnostic motifs, which we assess quantitatively (Fidelity, Sparsity) and through qualitative case studies. These results indicate that decoupling behavioural signatures from adversarial social positioning is valuable for modern bot detection, and that combining accuracy with interpretable evidence supports deployment in real-world moderation settings.
Social bot detection is vital for protecting online platforms from misinformation and manipulation. In recent research, graph neural networks (GNNs) have emerged as a powerful approach, since they leverage relational patterns and social interactions to identify coordinated bot behaviors. However, two key challenges arise from the nature of real-world social networks: First, bots often actively interact with human users through follows, replies, and mentions, creating numerous ''heterophilous'' edges, i.e., connections between different classes. These cross-class ties disrupt the homophily assumption underlying many GNNs, causing messagepassing to propagate and amplify errors. Second, due to the high cost and time required for manual annotation, social media platforms typically contain a large proportion of unlabeled data, with only a small fraction labeled for bot detection. Unlabeled data are often underutilized, making supervision sparse. To address this, we propose OTPCL (Optimal Transport Driven Pseudo-Labeling with Contrastive Learning), a plug-in framework for GNN-based social bot detection. OTPCL first employs contrastive learning to obtain well-separated node representations. It then formulates pseudolabel assignment as an optimal transport problem, which simultaneously generates pseudo-labels and quantifies their reliability via transport scores. These scores guide two key mechanisms: selective removal of unreliable heterophilous edges to purify the graph structure, and reducing the influence of pseudo-labels with transport scores below the dynamic threshold. Extensive experiments on three widely used benchmark datasets demonstrate that OTPCL consistently improves the detection performance across six different GNN backbones, showing strong robustness and generalization in both low-labeled and fully-labeled scenarios.
Ruixuan Xu, Mengting Hu, Xinqi Yang et al.· Proceedings of the 32nd ACM...· 0 citations
SNBot is proposed, a novel social bot detection framework that explicitly models the discrepancy between node self-representations and their neighborhood embeddings and consistently outperforms state-of-the-art methods.
Qilong Lin, Jingya Zhou· Annual International ACM SIG...· 0 citations
: Social network platforms have become primary channels for information dissemination, yet they are increasingly exploited by anomalous users such as bots, fake accounts, and coordinated disinformation spreaders. These malicious actors manipulate public opinion, spread misinformation and undermine platform integrity, posing severe threats to the security of the online ecosystem. Accurate detection of such users is challenging because they often organize into sophisticated high-order connection patterns that extend beyond local neighborhoods. Existing methods address this by either injecting predefined motifs as handcrafted features, which lack flexibility to discover unknown patterns, or employing higher-order Graph neural networks (GNNs) at prohibitive costs. Crucially, neither method treats structural information as learnable knowledge that can be automatically acquired from data and explicitly represented. To bridge this gap, we propose SparseGNN, a structural-knowledge-enhanced framework for anomalous user detection. It regards atomic subgraph patterns as fundamental, learnable units of structural knowledge. This framework is concatenated with original node features and fed into any standard GNN, without modifying the backbone architecture. Experiments on real-world datasets demonstrate that SparseGNN improves the accuracy and F1-score of standard GNNs for anomalous users detection without requiring predefined patterns, while maintaining linear complexity. Because the learned atomic patterns capture global high-order topology, the resulting structural knowledge representation is inherently less sensitive to localized edge perturbations, incidentally conferring improved stability under adversarial structural attacks.
Zehan Li, Yingyi Li, Zhiwei Tang et al.· Computers, Materials & C...· 0 citations
Hierarchical Interaction MOdeling for zero-shot generalist GAD enables anomaly detection across diverse graph domains without retraining or access to target-domain supervision by modeling the evolutionary trajectories of node representations across hierarchical structural depths, thereby capturing interaction patterns that exhibit strong cross-domain stability.
Xiangping Zheng, Xuan Feng, Bo Wu et al.· Proceedings of the 32nd ACM...· 0 citations
Social bot detection is crucial for maintaining the security and integrity of online social networks (OSNs). Although graph-based methods have achieved state-of-the-art performance, rapid advances in large language models have made bots increasingly similar to humans in the textual modality, while some bots also adopt diverse camouflage strategies in the structural modality. As a result, social bots exhibit pronounced individual-level heterogeneity in camouflage behavior, causing the discriminative power of different modalities to vary substantially across accounts. Existing multimodal methods typically rely on unified fusion strategies, which are insufficient to handle such sample-specific variation and may lead to misclassification when one modality is heavily camouflaged. Moreover, they generally lack an effective mechanism for jointly optimizing semantic and structural representations. To address these issues, we propose a camouflage-heterogeneity-aware decision fusion framework for social bot detection (BotCHF). In the encoding stage, BotCHF adopts an alternating collaborative optimization strategy that periodically injects fine-tuned semantic features into the graph encoder, thereby progressively aligning semantic encoding with structural learning. In the fusion stage, it maintains separate text and graph branches and adaptively weights their predictions for each account, enabling decision fusion that responds to account-specific variation in modality reliability. Extensive experiments on three real-world datasets demonstrate that BotCHF consistently outperforms strong baselines. Further analysis of fusion weights reveals substantial cross-dataset differences in modality preference, highlighting the necessity of explicitly modeling camouflage heterogeneity for robust social bot detection.
Xiaoyu Wang, S. Shi, Kai Qiao et al.· Journal of King Saud Univers...· 0 citations
Graph-based fraud detection, which identifies fraudulent and benign entities on graph-structured data, has shown strong potential in combating sophisticated fraud and attracted growing research attention. However, existing methods face two critical bottlenecks. First, increasingly complex fraud camouflage: fraudsters conceal collusive behaviors via multi-hop connections and deliberately link to benign nodes, preventing traditional models from capturing high-order patterns and causing feature homogenization of fraud nodes. Second, severe class imbalance: fraud nodes account for a tiny proportion of the graph, and weak fraud signals are easily overwhelmed by massive benign node information. To address these challenges, we propose HDN-GFD, a novel fraud detection framework integrating high-order hypergraph modeling and dynamic neighborhood aggregation. Specifically, we design a dual-dimensional hypergraph construction mechanism that upgrades pairwise connections to multi-node collaborative associations along structural and feature dimensions to capture high-order collusive relationships. We then develop an anomaly probability-guided dynamic aggregation strategy, which estimates node anomaly scores via node-subgraph feature consistency and adaptively aggregates neighborhood information from benign and fraudulent perspectives. This design decouples camouflage-induced confounding signals and amplifies minority fraud features, mitigating the adverse impact of class imbalance. Extensive experiments on four real-world datasets demonstrate that HDN-GFD consistently outperforms state-of-the-art baselines, verifying the effectiveness and superiority of our method.
Junzheng Li, Hongtao Yu, Ruiyang Huang et al.· Journal of King Saud Univers...· 0 citations