Detecting social media bots is essential for maintaining the security and trustworthiness of social networks. While contemporary graph-based detection methods demonstrate promising results, their practical application is limited by label reliance and poor generalization capability across diverse communities. Generative Graph Self-Supervised Learning (GSL) presents a promising paradigm to overcome these limitations, yet existing approaches predominantly follow the homophily assumption and fail to capture the global patterns in the graph, which potentially diminishes their effectiveness when facing the challenges of interaction camouflage and distributed deployment in bot detection scenarios. To this end, we propose BotHP, a generative GSL framework tailored to boost graph-based bot detectors through heterophily-aware representation learning and prototype-guided cluster discovery. Specifically, BotHP leverages a dual-encoder architecture, consisting of a graph-aware encoder to capture node commonality and a graph-agnostic encoder to preserve node uniqueness. This enables the simultaneous modeling of both homophily and heterophily, effectively countering the interaction camouflage issue. Additionally, BotHP incorporates a prototype-guided cluster discovery pretext task to model the latent global consistency of bot clusters and identify spatially dispersed yet semantically aligned bot collectives. Extensive experiments on two real-world bot detection benchmarks demonstrate that BotHP consistently boosts graph-based bot detectors, improving detection performance, alleviating label reliance, and enhancing generalization capability.

Recent advances in Large Language Models (LLMs) have shown promise in function-level code generation, yet repository-level software engineering tasks remain challenging. Current solutions predominantly rely on proprietary LLM agents, which introduce unpredictability and limit accessibility, raising concerns about data privacy and model customization. This paper investigates whether open-source LLMs can effectively address repository-level tasks without requiring agent-based approaches. We demonstrate this is possible by enabling LLMs to comprehend functions and files within codebases through their semantic information and structural dependencies. To this end, we introduce Code Graph Models (CGMs), which integrate repository code graph structures into the LLM's attention mechanism and map node attributes to the LLM's input space using a specialized adapter. When combined with an agentless graph RAG framework, our approach achieves a 43.00% resolution rate on the SWE-bench Lite benchmark using the open-source Qwen2.5-72B model. This performance ranks first among open weight models, second among methods with open-source systems, and eighth overall, surpassing the previous best open-source model-based method by 12.33%.

Albeit presenting great capabilities for graph node representations, Graph neural networks (GNNs) face biases and discrimination issues. Existing solutions mostly focus on one aspect of fairness, failing to thoroughly consider the multiple dimensions of fairness. To break this limitation, we propose a novel fairness-aware framework, FacGNN, which establishes connections between group fairness, counterfactual fairness, and stability fairness for the first time. Specifically, FacGNN enhances both group fairness and counterfactual fairness while extending stability metrics beyond prior works. FacGNN employs a phased nested iterative training process with self-supervised contrastive learning, to model fairness relationships and improve discriminatory detection in adversarial frameworks. Experimental results show that FacGNN outperforms previous methods across group fairness, counterfactual fairness, and model stability while maintaining model performance.

Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks. The social graph, constructed from social network interactions, contains benign and bot accounts that influence each other. However, previous graph-based detection methods that follow the transductive message-passing paradigm may not fully utilize hidden graph information and are vulnerable to adversarial bot behavior. The indiscriminate message passing between nodes from different categories and communities results in excessively homogeneous node representations, ultimately reducing the effectiveness of social bot detectors. In this paper, we propose \SEBot, a novel multi-view graph-based contrastive learning-enabled social bot detector. In particular, we use structural entropy as an uncertainty metric to optimize the entire graph's structure and subgraph-level granularity, revealing the implicitly existing hierarchical community structure. And we design an encoder to enable message passing beyond the homophily assumption, enhancing robustness to adversarial behaviors of social bots. Finally, we employ multi-view contrastive learning to maximize mutual information between different views and enhance the detection performance through multi-task learning. Experimental results demonstrate that our approach significantly improves the performance of social bot detection compared with SOTA methods.

Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture make it intricate to devise an efficient cross-platform and cross-model detection framework. In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. We devise a GAN-based federated knowledge distillation mechanism for efficiently transferring knowledge of data distribution among clients. In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client’s local model. We leverage local discriminator to enable customized model design and use local generator for data enhancement with hard-to-decide samples. Local training is conducted as multi-stage adversarial and contrastive learning to enable consistent feature spaces among clients and to constrain the optimization direction of local models, reducing the divergences between local and global models. Experiments demonstrate that FedACK outperforms the state-of-the-art approaches in terms of accuracy, communication efficiency, and feature space consistency.

The realm of Synthetic Aperture Radar (SAR) ship detection has witnessed widespread adoption of deep learning, owing to its exceptional detection accuracy and end-to-end capabilities. Despite these advantages, the current SAR ship target detection methods still face the challenge of detecting small-scale targets and are difficult to be deployed on satellite platforms due to their complex models and huge computational effort. To overcome these problems, based on the YOLOv5 architecture, we present a super lightweight and efficient SAR ship target detection method named SLit-YOLOv5. Our proposed model comprises two essential components, IMNet and Slim-BiFPN. IMNet serves as the backbone feature extraction network, significantly enhancing the feature extraction capability while reducing the number of parameters by half. Slim-BiFPN achieves adaptive fusion of multi-scale features with fewer parameters. To validate the proposed model, we conducted an experimental evaluation on the SAR ship detection dataset (SSDD), and the results show that our SLit-YOLOv5 model outperforms the currently popular lightweight SAR ship target detection methods with high detection accuracy, low floating-point operations, and very few params.

Social bots are referred to as the automated accounts on social networks that make attempts to behave like humans. While Graph Neural Networks (GNNs) have been massively applied to the field of social bot detection, a huge amount of domain expertise and prior knowledge is heavily engaged in the state-of-the-art approaches to design a dedicated neural network architecture for a specific classification task. Involving oversized nodes and network layers in the model design, however, usually causes the over-smoothing problem and the lack of embedding discrimination. In this article, we propose RoSGAS , a novel R einf o rced and S elf-supervised G NN A rchitecture S earch framework to adaptively pinpoint the most suitable multi-hop neighborhood and the number of layers in the GNN architecture. More specifically, we consider the social bot detection problem as a user-centric subgraph embedding and classification task. We exploit the heterogeneous information network to present the user connectivity by leveraging account metadata, relationships, behavioral features, and content features. RoSGAS uses a multi-agent deep reinforcement learning (RL), 31 pages. mechanism for navigating the search of optimal neighborhood and network layers to learn individually the subgraph embedding for each target user. A nearest neighbor mechanism is developed for accelerating the RL training process, and RoSGAS can learn more discriminative subgraph embedding with the aid of self-supervised learning. Experiments on five Twitter datasets show that RoSGAS outperforms the state-of-the-art approaches in terms of accuracy, training efficiency, and stability and has better generalization when handling unseen samples.