Accurately identifying IoT devices connected to a network is crucial for improving network management and ensuring network security. As IoT devices increasingly move across institutional boundaries, identifying devices that migrate among institutions has become a significant challenge. However, existing studies assume that IoT devices remain stationary. As a result, they are inadequate for addressing the security risks introduced by device mobility across institutions. Therefore, we propose an IoT device identification framework named DFGLIoT. The framework adopts a decentralized fully connected architecture to enable knowledge sharing among institutions. This design avoids single points of failure while effectively supporting the identification of cross-institutional mobile IoT devices. We model the communication traffic between IoT devices and their gateways as communication interaction graphs. These graphs provide a comprehensive view of the interaction process between the communicating parties. Based on this representation, we design a graph classifier that integrates a dual-scale dependency modeling module with a spatial feature extraction module. The classifier captures interaction patterns in the communication traffic and constructs behavior fingerprints of IoT devices, enabling accurate device identification. Experimental results on three public datasets demonstrate the effectiveness of DFGLIoT in identifying IoT devices that move across institutions. The source code can be accessed at https://github.com/traveler-wang/DFGLIoT.

Federated learning is an emerging machine learning paradigm that effectively alleviates the data silo problem by distributing the model training process to multiple data holders. However, data from real-world mobile applications often has multi-label and presents a long-tailed distribution, where labels are generally non-independent and non-identically distributed, thereby increasing the challenges caused by data heterogeneity. To address the above problems, we propose a Federated Chain Context Optimization (FedCCO) for long-tailed multi-label image classification. Inspired by the success of Chain of Though (CoT) in enhancing the semantic expressive ability of models, this method fine-tunes the CLIP model using semantic descriptive vectors generated by the Chain Context Optimization (ChCoOp) to establish semantic correlations between head and tail classes across clients, which improves the ability of the model to recognize tail classes. The experimental results show that the FedCCO achieves satisfactory performance in long-tailed multi-label image classification in federated learning on VOC-LT and COCO-LT datasets.

Understanding and modeling dynamic inter-stock dependencies is crucial for effective investment decisions. While graph-based methods have demonstrated potential in capturing stock relationships, existing approaches based on static or discrete-time dynamic graphs fail to address the continuous evolution of market dynamics. In this work, we propose CFC-GMixer, a novel continuous-time graph framework that synergizes multi-scale temporal features with graph-based continuous-time modeling. Our architecture consists of two main components: (1) a Time Mixer module that extracts multi-scale temporal features to capture market dynamics at different time horizons, enabling effective graph relationship modeling, (2) a unified CFC-GCN module that leverages these hierarchical features to simultaneously model adaptive inter-stock relationships and their continuous temporal evolution through Graph Convolutional Networks (GCN) and Closed-Form Continuous-Time Networks (CFC). Experiments on three real-world datasets demonstrate that CFC-GMixer outperforms state-of-the-art methods in both accuracy and precision.

Stock recommendation plays a critical role in financial investment decision-making. In real-world markets, stocks exhibit complex interdependencies through correlated price movements. Existing approaches derive stock relationships either from fundamental information (e.g., industry categories) or price temporal patterns. However, these methods face limitations: domain expertise-based approaches fail to capture implicit correlations, while short-term relationship modeling suffers from inadequate temporal feature representation. To address these challenges, we propose ALSGCN, an Attention-based Long- and Short-term Graph Convolutional Network for stock recommendation. ALSGCN captures dynamic stock relationships through two key components: (1) a market-aware mechanism that models long-term dependencies by incorporating the influence of large-cap stocks, and (2) an attention-based temporal feature extraction module that adaptively weights information across time steps. These complementary relationship representations are integrated through a multichannel graph attention module for effective feature learning. Extensive experiments on two real-world datasets demonstrate that ALSGCN consistently outperforms state-of-the-art baselines across most evaluation metrics. Code is available at https://github.com/hongnianwang/ALSGCN.

Federatedlearning has garnered significant attention in the Internet of Things and healthcare applications due to its ability to train a shared global model across distributed clients. However, imbalanced data distribution leads to model discrepancies among clients. Most existing methods adopt implicit alignment strategies while overlooking explicit modeling of geometric and directional discrepancies in feature representations, which undermines local model optimization. To address this issue, we propose a method of representation deviation alignment in federated learning, which projects features onto the principal feature space to measure deviations between local and global feature representations explicitly. Specifically, Federated learning with Representation Deviation Alignment (FedRDA) employs a feature encoder to extract compact features and construct unbiased principal feature spaces for global and local models. Then, the residual projection in the feature space serves as a quantitative measure of the representation deviation, effectively capturing the latent direction differences between models. Besides, we introduce a representation consistency alignment strategy, which ensures that the distribution of local client features becomes more uniform within the global feature space. Extensive experiments on SVHN, CIFAR-10, CIFAR-100, Tiny-ImageNet, and GC10 demonstrate that FedRDA effectively reduces the classifier bias caused by representational differences.

Detecting drug side effects is crucial in development of drugs. As publicly available biomedical data expands, researchers have devised numerous computational methods for predicting drug-side effect associations (DSAs). Among these, network-based approaches have gained significant attention in the biomedical field. However, the challenge of data scarcity poses a significant hurdle for existing DSAs prediction models. While various data augmentation methods have been created to solve the proble, most rely on random alterations to the original networks, neglecting the causality of DSAs’ existence, thus impacting the predictive performance negatively. In this paper, we introduce a counterfactual inference-based data augmentation method to enhance performance. First,a heterogeneous information network (HIN) is construct by integrating multiple biomedical data sources. We employ community detection on the HIN to preform a counterfactual inference-based method, deriving augmented links and an augmented HIN. Subsequently, we apply a meta-path-based graph neural network to obtain high-quality representations of drugs and side effects, enabling the prediction of DSAs. Our comprehensive experiments confirm the effectiveness of this counterfactual inference-based data augmentation for DSAs prediction.

Side effects of drugs have gained increasing attention in the biomedical field, and accurate identification of drug side effects is essential for drug development and drug safety surveillance. Although the traditional pharmacological experiments can accurately detect the side effects of drugs, the identifying process is time-consuming, costly, and may lead to incomplete identification of side effects. With the expanding of various biomedical databases, many computational methods have been developed for the task of drug-side effect associations (DSAs) prediction. However, existing methods have the following three drawbacks: 1). multiple drug-related databases are not fully used; 2). the complex semantics among drugs and side effects are not effectively captured; 3). the explainability of the predicted DSAs is missed for most existing methods. Therefore, there is an urgent need to find a more effective method for predicting DSAs. To address these issues, we propose a novel meta-path-based graph neural network model for drug-side effect associations prediction (MPGNN-DSA). In MPGNN-DSA, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a meta-path-based feature learning module is utilized for learning high-quality representations of drugs and side effects by capturing the semantics contained in meta-paths of the constructed HIN. With the learned features, the prediction module is conducted to derive the predicted side effects for drugs. In addition, the explainability of the predicted DSAs can be provided as well with the semantics contained in meta-paths. We conduct comprehensive experiments, and the results demonstrate the effectiveness of MPGNN-DSA, suggesting that the proposed method will be a feasible solution to the task of DSAs prediction.

Drug side effect is an important entity in the biomedical field, and identifying the association of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. Traditional side effect discovery methods are mainly based on pharmacological experiments. These methods can detect the side effects of some drugs, but the identification process is time-consuming, expensive, and fails to identify some rare side effects. In recent years, with the expansion of massive biomedical data, computational-based methods are widely developed and applied for the task of drug-side effect association(DSA) prediction. However, existing methods cannot fully utilize public biomedical databases, and the complex semantic associations between drugs and side effects are not effectively captured, which leads to suboptimal model prediction performance. In this study, we develop a novel meta-path-based graph neural network model for drug-side effect association prediction. In the proposed model, we first construct a heterogeneous information network(HIN) by fusing multiple biological datasets. And then, a novel meta-path-based feature learning module is designed to learn high-quality representations of drugs and side effects. Finally, with the learned features, the prediction module utilizes a fully connected neural network to make prediction. In addition, comprehensive experiments is conducted, the results demonstrate the effectiveness of our model, indicating that the method will be a viable approach for DSA prediction tasks.