The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions.

Recommendation systems greatly improve user experience with personalized suggestions. Much research utilizes deep learning to extract user interest features, yet often neglects their real-time and dynamic aspects. This paper proposes a joint learning-based recommendation model (JLS-Rec) for e-commerce platforms, integrating both short-term and long-term user interests. First, for users' long-term interests, the paper suggests mining more detailed interests from user behavior sequences. This method decouples the behavior sequence in horizontal and vertical directions using a Convolutional Neural Network to learn the user's long-term interests. Second, for users' short-term interests, the JLS-Rec method learns feature transformations on users' recent behavior sequences by stacking multiple self-attention mechanisms, resulting in dynamic representations of the user's short-term interests at the current stage. Finally, based on the principle of prioritizing short-term memory without neglecting long-term interests, the paper proposes a joint learning framework with dual embeddings to balance the two characteristics of user long-term and short-term interests. This framework generates accurate recommendation results while utilizing these two interest features to predict user feedback on products. The experimental results demonstrate that the model effectively mines the long-term and short-term interest information of users in the features, thereby improving the recommendation accuracy of e-commerce platforms.

This study is inspired by the current image restoration technology. If we regard the users participating in the rumor as image pixels, similar to social networks, the recovery of pixel data is affected by the pixels themselves and neighbor pixels, then the prediction of user behavior in the rumor diffusion can be regarded as the process of image restoration for pixel-blurred user behavior images. We first propose a diffusion2pixel algorithm that transforms the user relationship network of topic diffusion into image pixel matrix. To cope with the diversity and complexity of the diffusion feature space, the user relationship network is reduced to a low-rank dense vectorization by representation learning before being pixelated by cutting and diffusion. Second, considering the competitive relationship between rumor and anti-rumor, transition matrix of rumor mutual influences is established by evolutionary game theory. A mutual influence model of rumor and anti-rumor is then proposed. Finally, we combine the transition matrix of rumor mutual influence into a simple prediction method Graph-CNN of rumor and anti-rumor topic diffusion based on dynamic iteration mechanism. Experiments confirmed the proposed model can effectively predict the group diffusion trends of rumor, and reflects the competitive relationship between rumor and anti-rumor.

Vertical federated learning can aggregate participant data features. To address the issue of insufficient overlapping data in vertical federated learning, this study presents a generative adversarial network model that allows distributed data augmentation. First, this study proposes a distributed generative adversarial network FeCGAN for multiple participants with insufficient overlapping data, considering the fact that the generative adversarial network can generate simulation samples. This network is suitable for multiple data sources and can augment participants’ local data. Second, to address the problem of learning divergence caused by different local distributions of multiple data sources, this study proposes the aggregation algorithm FedKL. It aggregates the feedback of the local discriminator to interact with the generator and learns the local data distribution more accurately. Finally, given the problem of data waste caused by the unavailability of nonoverlapping data, this study proposes a data augmentation method called VFeDA. It uses FeCGAN to generate pseudo features and expands more overlapping data, thereby improving the data use. Experiments showed that the proposed model is suitable for multiple data sources and can generate high-quality data.

Rumor spreading in social networks involves complex dynamic causes. This paper constructs a new rumor spreading dynamics model based on evolutionary game theory to account for the possible skepticism of users during the dissemination of rumors, and introduces control theory for the directional management of public opinion in social networks. Firstly, based on the infectious disease dynamics model, we introduce users who exhibit skepticism when exposed to rumor information and continue to pay attention to it, classifying them as in a rumor-suspecting state during the rumor propagation process. Taking into account the impact of rumor information on users, as well as their intrinsic tendency to seek profit in the face of rumors, we quantify the influence of rumor news. This paper innovatively introduces the “rumor-suspecting state” into the traditional rumor propagation model, enabling a more comprehensive representation of skeptical user behaviors during rumor dissemination. By combining this with evolutionary game theory, we construct the driving force mechanism for users' rumor propagation, providing a foundation for understanding the transformation of users' states within the rumor-suspecting context. Secondly, to reduce the impact of rumor information and limit its spread, we develop a hybrid control strategy that combines two approaches: prevention and isolation. After implementing these hybrid control measures, we address the imbalance between control costs and effectiveness by establishing an optimal control problem with constraints. This aims to achieve optimal control with time-varying properties, and we theoretically derive the optimal solution to minimize costs. Finally, considering the complexity of rumor information and the need for effective rumor control, we propose an improved model of rumor propagation dynamics that combines the infectious disease model with optimal control theory. This model defines state transfer equations based on multiple user states and optimal control.The effectiveness of the control strategy is validated through theoretical proofs and experiments, and the impact of various factors on information diffusion is analyzed. On a real dataset we show that the model can effectively explain the diffusion process of complex rumor information in the network and manage it.