SNBot: Modeling Self–Neighborhood Representation Discrepancy for Social Bot Detection

OTPCL: Optimal Transport Driven Pseudo-Labeling with Contrastive Learning for Social Bot Detection

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.

XHBot: eXplainable Heterophily-aware Graph Neural Networks for Social Bot Detection

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.

Rethinking Generalization in Graphs: A Hierarchical Interaction Perspective for Generalist Detection

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.

Balancing the truth: social media misinformation detection via GraphSAGE and feature diversity

Results highlight the value of integrating relational graph structures with complementary feature representations for misinformation detection, and provide a foundation for future extensions, including federated learning, explainability techniques, and cross-domain applications in multilingual contexts.

Disentangled Hypergraph Network with Implicit Structure Learning for Mobility Social Relationship Inference

Embedding-Space Orthogonal Decomposition for Robust Social Recommendation

Graph-based social recommendation leverages both the interaction graph and the social graph to model user preferences, especially under sparse feedback. However, users' intricate social behaviors may introduce mismatched social ties that contaminate user representations and harm the models' robustness. The majority of existing methods mitigate this by pruning, rewiring, or assigning edge-wise weights before social aggregation. From users' historical behaviors, we observe that a social neighbor often overlaps with the target user on specific interests but differs in others. Thus, using a single weight for each social connection is insufficient, as it only scales the overall message intensity and fails to selectively suppress the misaligned components within the aggregated message. To fill this gap, we propose Orthogonal Decomposition for Social Recommendation (ODSR), an embedding-space framework that orthogonally decomposes the aggregated social message into an aligned component and an orthogonal deviation, and learns a dimension-wise vector gate to regulate the deviation under ranking supervision. Additionally, we introduce a contrastive regularizer that perturbs representations along deviation directions to enhance robustness against imperfect social signals. Extensive experiments on three datasets show that ODSR consistently outperforms strong baselines, and additional analyses verify the effectiveness of selectively gating the orthogonal deviation.