As user–item interaction information is typically limited, collaborative filtering (CF)-based recommender systems often suffer from the data sparsity issue. To address this issue, recent recommender systems have turned to graph neural networks (GNNs) due to their superior performance in capturing high-order relationships. Furthermore, some of these GNN-based recommendation models also attempt to incorporate other information. They either extract self-supervised signals to mitigate the data sparsity problem or employ social information to assist with learning better representations under a social recommendation setting. However, only a few methods can take full advantage of these different aspects of information. Based on some testing, we believe most of these methods are complex and redundantly designed, which may lead to sub-optimal results. In this paper, we propose SSGCL, which is a recommendation system model that utilizes both social information and self-supervised information. We design a GNN-based propagation strategy that integrates social information with interest information in a simple yet effective way to learn user–item representations for recommendations. In addition, a specially designed contrastive learning module is employed to take advantage of the self-supervised signals for a better user–item representation distribution. The contrastive learning module is jointly optimized with the recommendation module to benefit the final recommendation result. Experiments on several benchmark data sets demonstrate the significant improvement in performance achieved by our model when compared with baseline models.

Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting the data. Additionally, when transportation information changes, downstream enterprises must promptly adjust their production plans to accommodate these alterations. The strict access controls of private blockchains can obstruct downstream enterprises from obtaining data, posing a challenge to the overall operational efficiency. In this paper, we propose an innovative logistics data protection scheme that employs private set intersection (PSI) and blockchain cross-chain technology to achieve data security. In our scheme, logistics companies within the logistics consortium are added as trusted agents to the public blockchain, enabling downstream enterprises to acquire logistics data integration from the public blockchain. Utilizing an RSA-based PSI protocol, our approach enhances exchange efficiency while protecting private data without transmitting additional information. We evaluate the performance of the proposed solution through a series of experiments, and the results demonstrate that our solution can achieve secure and efficient logistics data exchange.

Session-based recommendation (SBR) aims to predict the next item a user may interact with based on an anonymous session, playing a crucial role in real-time recommendation scenarios. However, existing SBR models struggle to effectively capture local session dependencies and global item relationships, while also facing challenges such as data sparsity and noisy information interference. To address these challenges, this paper proposes a novel Multi-View Graph Contrastive Learning Neural Network (MVGCL-GNN), which enhances recommendation performance through multi-view graph modeling and contrastive learning. Specifically, we construct three key graph structures: a session graph for modeling short-term item dependencies, a global item graph for capturing cross-session item transitions, and a global category graph for learning category-level relationships. In addition, we introduce simple graph contrastive learning to improve embedding quality and reduce noise interference. Furthermore, a soft attention mechanism is employed to effectively integrate session-level and global-level information representations. Extensive experiments conducted on two real-world datasets demonstrate that MVGCL-GNN consistently outperforms state-of-the-art baselines. MVGCL-GNN achieves 34.96% in P@20 and 16.50% in MRR@20 on the Tmall dataset, and 22.59% in P@20 and 8.60% in MRR@20 on the Nowplaying dataset. These results validate the effectiveness of multi-view graphs and contrastive learning in improving both accuracy and robustness for session-based recommendation tasks.

In Vehicular Ad-hoc Networks (VANETs), federated learning (FL) enables collaborative training a global intelligent transportation model without sharing vehicles’ raw data. Achieving model convergence in VANETs requires multiple rounds of FL for aggregation and updates. To improve the efficiency of model convergence, researchers explore methods of multi-round aggregation. The latest work, Flamingo (S&P 2023), proposes a multi-round aggregation scheme based on a reusable key mechanism. Its application in VANETs with dynamic network structures enhances the efficiency of model training. However, this scheme has a defect: vehicles cannot verify the correctness of the aggregation result. Once a semi-trusted server returns incorrect aggregation results due to computational errors, it may reduce the model’s accuracy or even cause training failure. In this scheme, we propose a verifiable multi-round aggregation scheme for FL in VANETs (VMFL), enabling vehicles to verify the aggregation results. Firstly, a multi-round verification mechanism is designed to reduce the number of interactions for vehicles by using reusable keys. Additionally, we propose a lightweight proof scheme that allows vehicles to verify the results with minimal computation, reducing the computational overhead of the verification process. Finally, a security analysis of VMFL is performed to demonstrate its security in cases of vehicle dropout. We validated the efficiency of VMFL across different datasets through experiments, showing no significant increase in time overhead compared to Flamingo, and demonstrating that its verification time is reduced by about 90% compared to the state-of-the-art scheme, thereby demonstrating the scheme’s usability.

Traditional node classification is typically conducted in a closed-world setting, where all labels are known during training, enabling graph neural network methods to achieve high performance. However, in real-world scenarios, the constant emergence of new categories and updates to existing labels can result in some nodes no longer fitting into any known category, rendering closed-world classification methods inadequate. Thus, open-world classification becomes essential for graph data. Due to the inherent diversity of graph data in the open-world setting, it is common for the number of nodes with different labels to be imbalanced, yet current models are ineffective at handling such imbalance. Additionally, when there are too many or too few nodes from unseen classes, classification performance typically declines. Motivated by these observations, we propose a solution to address the challenges of open-world node classification and introduce a model named OWNC. This model incorporates a dual-embedding interaction training framework and a generator–discriminator architecture. The dual-embedding interaction training framework reduces label loss and enhances the distinction between known and unseen samples, while the generator–discriminator structure enhances the model’s ability to identify nodes from unseen classes. Experimental results on three benchmark datasets demonstrate the superior performance of our model compared to various baseline algorithms, while ablation studies validate the underlying mechanisms and robustness of our approach.