The Internet of Everything (IoE) has recently become a hot topic. With the development of Internet of Things (IoT) technology, people can connect to networks in increasingly diverse ways. The surge in users, devices, and requests poses significant challenges to network capacity and backhaul links. Content caching technology has long been considered a promising approach to improving network performance. However, existing methods still have room for improvement in terms of content transmission efficiency and user access latency. To address these issues, this paper proposes an Influence-Driven Content Caching (IDCC) method. Specifically, based on a caching strategy of “caching content that is likely to have the greatest future influence on the most influential edge devices", this paper designs a comprehensive framework encompassing content selection, updating, and placement to optimize content caching efficiency, enhance network spectral efficiency, and improve user’s quality of experience (QoE). First, a content selection strategy based on the popularity dynamics prediction method is developed by utilizing graph neural networks and contrastive learning to model heterogeneous data. Second, a content update mechanism for cached content and key caching information is designed based on the popularity of content and Near-Field Communications (NFC) between users. Furthermore, interconnected network devices are represented as a graph, and the communication influence of key network nodes is predicted using autoencoders and graph neural networks to identify the optimal caching nodes for maximizing benefits. Finally, extensive experiments show that the proposed IDCC method offers significant advantages in reducing network latency and improving network utilization.

Information diffusion prediction is a complex task due to the dynamic of information substitution present in large social platforms, such as Weibo and Twitter. This task can be divided into two levels: the macroscopic popularity prediction and the microscopic information diffusion prediction (who is next), which share the essence of modeling the dynamic spread of information. While many researchers have focused on the internal influence of individual cascades, they often overlook other influential factors that affect information diffusion, such as competition and cooperation among information, the attractiveness of information to users, and the potential impact of content anticipation on further diffusion. To address this issue, we propose a multiscale information diffusion prediction with minimal substitution (MIDPMS) neural network. This model simultaneously enables macroscale popularity prediction and microscale diffusion prediction. Specifically, information diffusion is modeled as a substitution system among different information. First, the life cycle of content, user preferences, and potential content anticipation are considered in this system. Second, a minimal-substitution-theory-based neural network is first proposed to model this substitution system to facilitate joint training of macroscopic and microscopic diffusion prediction. Finally, extensive experiments are conducted on Weibo and Twitter datasets to validate the performance of our proposed model on multiscale tasks. The results confirmed that the proposed model performed well on both multiscale tasks on Weibo and Twitter.

One of the key challenges of current online social platforms is predicting the size of information cascades, also known as popularity prediction or cascade prediction. Accurate popularity prediction can benefit various fields, including news distribution, market decisions, and rumor detection. However, existing popularity prediction approaches concentrate more on the historical sequences of single messages, overlooking the interactions between message diffusion and the dynamic nature of social networks, which limits the timeliness and accuracy of predictions. To address this, we propose an online popularity prediction service based on minimal substitution reinforcement learning calledMSRL. Specifically, we explore a substitution theory and design a minimal substitution reinforcement learning method that models diffusion as message substitution and considers mutual information diffusion. That helps the model gain a broader perspective, allowing it to fully exploit the cooperative, competitive, or dependent relationships between information diffusions. Furthermore, the reinforcement learning scheme enables the service to dynamically adjust its parameters to respond to the dynamic social network environment in real-time. Finally, extensive experiments on real-world datasets show that the MSRL outperforms state-of-the-art methods regarding accuracy and service agility.

In social media platforms such as Weibo, Twitter, and Facebook, a variety of information is diffused daily. Exploring and exploiting the diffusion patterns in this information play crucial roles in areas such as viral marketing, recommendation systems, and public opinion management. However, the diffusion of this information is not merely sequential propagation among users, as most researchers assume. When we observe the diffusion of information in the entire network from a macroscopic perspective, we discover that these phenomena of information diffusion exhibit a series of interconnected relationships, such as alternation or dependency. In traditional methods of information diffusion prediction (IDP), these aspects are often overlooked. To address this, we introduce a substitution theory of information diffusion, minimal substitution (MS), and we combine it with neural networks to design a network model known as MSNN. First, the incorporation of MS theory enables our model to effectively capture the complex interrelations among pieces of information. Second, we analyze the relationship of the multi-scale IDP task, develop a one-step MS-based microscopic IDP method and a dynamic MS-based macroscopic IDP method, and utilize these two methods for joint training to achieve multi-scale prediction. Finally, we validate the accuracy of the proposed MSNN model through training on two real-world datasets with different growth patterns.

With the global epidemic of the COVID-19, various rumors spread wantonly on social networks, which has seriously affected the stability and harmony of the entire society. To purify the network environment, some researchers have proposed to fight rumors from the perspectives of tracing the source of rumors, detecting the authenticity of information, and predicting explosive fake news. But their works are fragmented, and their performance are not significant. So we need strong antirumor methods to fight rumors. To this end, this article proposes a more comprehensive antirumor mechanism, which can realize rumors source location, rumor detection, and popularity prediction (RLDP). In particular, in the task of localization, we propose graph neural network-based method, which does not need to specify the underlying propagation mode and the number of rumor sources; in the task of detection, utilizing lightGBM, we construct a rumor detection model; in the task of popularity prediction, we construct a model based on contrastive learning while considering user engagements and information propagation, and the text of rumor. Finally, we verify the performance of the proposed RLDP by conducting extensive experiments.

How to localize the rumor source is a common interest of all sectors of the society. Many researchers have tried to use deep-learning-based graph models to detect rumor sources, but they have neglected how to train their deep-learning-based graph models in thenoisysocial network environmentefficiently. Especially for deep learning models, the performance relies on the data scale. However, even though its known that a substantial amount of rumor data distributed across multiple edge servers (e.g., cross-platform), due to conflicting business interests, its challenging to coordinate all parties to train a model driven by many samples while avoiding moving data. Federated learning, is an effective technique to bridge this gap. Therefore, this paper proposes aDistributedRumorSourceDetection viaBoostedFederatedLearning (DRSDBFL). Specifically, this paper proposes an effective rumor source detection method based on a deep-learning-based graph model with a denoising module. To the best of our knowledge, we are the first to attempt to the use of a denoising module to reduce the noisy effects of social networks. Then, we propose a novel boosted federated learning mechanism through boosting the high-quality edge worker to improve the training efficiency. Finally, the effectiveness of the proposed method is verified by extensive experiments.

Content caching in mobile networks is a highly promising technology for reducing traffic load latency and energy consumption levels. Its fundamental goal is to satisfy the supply-and-demand relationships between content providers and content-requesting users. However, previous research primarily focused on the optimization goals of mobile network operators, and although these caching strategies yield improved latency and energy consumption levels, they fall short of satisfying the diverse content demands of users in real-world scenarios. Therefore, this paper proposes a diversity-driven proactive caching strategy that considers multiple stakeholders' requirements, in which the cache hit rate, cache gain for network operators, and content diversity are jointly optimized. Specifically, a novel improved Latent Dirichlet Allocation (LDA) is designed for radio access network caching, which enables diverse topic associations. For user device caching at the network edge, a Gradient-Guided Contrastive Learning (GGCL) is proposed to optimize the multiple objectives of cache systems with limited labeled data resources. Finally, extensive experiments conducted on the MovieLens dataset demonstrate that the proposed method significantly outperforms other methods in various aspects, including content diversity, the cache hit rate, and some network performance metrics, such as the traffic load and cache gain.

Supervising illicit activities on blockchain networks, such as money laundering, fraud, extortion, Ponzi schemes, and funding for terrorist organizations, presents significant challenges. Emerging machine learning methods for detecting abnormal transactions face hurdles due to high labeling costs, limited labeled data, and data imbalance. To address this, this paper proposes aMinimalSubstitution-basedLabelPropagation(MSLP) model to provide more labeled data to balance the graph data and complement the sample for anomalous transaction detection service in the blockchain networks. As far as we know, MSLP is the first method that utilizes the minimal substitution theory from the social computing field to find more abnormal transactions with under-labeling budget constraints. This approach has the potential to obtain more high-quality labeled data with minimal computational cost by utilizing a small amount of labeled graph data. Then, a label evaluation mechanism is proposed to decide the number of samples to be adopted for each class, ensuring the performance of downstream graph neural networks. Finally, extensive experiments were conducted and the proposed model improved the F1 score of illegal transaction node detection by 2.6% to 8.2%.

Due to the significant Influence of social networks, information diffusion prediction, which aims to study the spread of messages among users, has become a crucial objective in various scenarios. Existing works have mostly attempted to integrate multiple source features such as social relationships and user preferences for prediction. Although it can improve the accuracy of predictions to some extent, there are still issues with complex models, low computational efficiency, and limited prediction performance. To address these challenges, this paper proposes a Gaussian-Noise-based Information Diffusion Prediction Model (GNIDP). As we know, we are the first to model the information diffusion in the social network as Gaussian noise diffusion, specifically, GNIDP utilizes Gaussian noise to simulate information diffusion patterns during information propagation, while capturing the evolving diffusion patterns over time across different time slots. Consequently, our model does not rely on social topology to achieve prediction, while improving the efficiency and accuracy of predictions. Experiments on two real social network datasets demonstrates that GNIDP achieves 13.54% relative gains over the best baseline on average. Therefore, the proposed model can be applied in scenarios where social topology is missing or difficult to obtain particularly in large-scale datasets requiring efficient diffusion prediction.

Due to the lack of centralized regulatory authorities, the cryptocurrency trading market has witnessed an increase in illicit activities. Recently, more and more researchers have started exploring the application of machine learning techniques to achieve anomaly detection in these transaction networks. However, in this transaction network, it’s hard to find the abnormal nodes because the labeling cost is high, without enough labeled and imbalanced data, which greatly limits the performance of the detection model. Therefore, this paper proposes a Minimal Substitution-based Label Propagation (MSLP) model to provide more labeled data to balance the graph data and complement the sample for downstream anomalous transaction detection in the blockchain networks. Specifically, MSLP is the first method that utilizes the minimal substitution theory from the social computing field to find more minority nodes from the unlabeled nodes. This approach has the potential to obtain more high-quality labeled data with minimal computational cost by utilizing a small amount of labeled graph data. Then, a label evaluation mechanism is proposed to decide the number of samples to be adopted for each class, ensuring the performance of downstream graph neural networks. Finally, extensive experiments were conducted and the proposed model improve the F1 score of illegal transaction node identification by 2.6% to 8.2%.

With the rise of the Industrial Internet of Things (IIoT), more and more industrial devices can be connected via the network. Data collection, processing, analysis, task execution, and other devices that can product network traffic volume are gradually being deployed to IIoT. However, under the limited spectrum resources and low-cost and low-energy production requirements of enterprises, how to ensure the interconnection and intercommunication of industrial networks while realizing the effective use of network communication resources is currently a hot topic. Among them, network traffic prediction is considered to be a very important task. The time variability and interpretability, especially the time-varying features of traffic sequences, greatly challenge this task. To address those, this article proposes a method calledFlow2graphto predict network traffic in IIoT. Specifically, some key segments, i.e., shapelets are extracted from the network traffic sequence according to time-varying traffic; then uses the relationship between the traffic sequence and shapelets to convert the flow into a shapelets conversion graph; Subsequently, the graph isomorphism network are used to learn the specificity of the flow sequence from different devices, thereby to predict its traffic value for a period of time in the future; finally, we conduct extensive experiments on real data to verify the effectiveness of the proposed method.

In the Cognitive Internet of Vehicles (CIoV), vehicles, road side units (RSU) and other key nodes have been equipped with more and more software to support intelligent transportation system (ITS), vehicle automatic control and intelligent road information services. Additionally, technological innovation forces the software in the CIoV to update and upgrade in time. However, escalation is critical to the safety, stability, and maintenance cost of transportation systems. It can be assumed that when the intelligent services supporting CIoV can realize self-perception and escalation, the cognitive ability and coordination ability of the entire CIoV will be greatly improved. To address this, we first propose a deep learning-based method for Software Escalation Prediction (SEP) in CIoV. Specifically, the pretraining mechanism of transformers in the field of natural language processing is combined with software upgrade-related events to dynamically model software sequence activities. To capture the event association in the software activities, we use graph modeling software’s state log and utilize a graph neural network (GNN) to learn the complex life activity rule of software. Finally, the above characteristics are deeply integrated. The proposed method has a 6%–8% improvement over the RoBERTa methods.

In the intelligent transportation system, the session data usually represents the users' demand. However, the traditional approaches only focus on the sequence information or the last item clicked by the user, which cannot fully represent user preferences. To address this issue, this paper proposes an Multi-aspect Aware Session-based Recommendation (MASR) model for intelligent transportation services, which comprehensively considers the user's personalized behavior from multiple aspects. In addition, it developed a concise and efficient transformer-style self-attention to analyze the sequence information of the current session, for accurately grasping the user's intention. Finally, the experimental results show that MASR is available to improve user satisfaction with more accurate and rapid recommendations, and reduce the number of user operations to decrease the safety risk during the transportation service.

With the spread of COVID-19 worldwide, people¡¯s production and life have been significantly affected. Artificial intelligence and big data technologies have been vigorously developed in recent years. It is very significant to use data science and technology to help humans in a timely and accurate manner to prevent and control the development of the epidemic, maintain social stability and assess the impact of the epidemic. This paper explores how data science can play a role from the perspectives of epidemiology, social networking, and economics. In particular, for the existing epidemic model SIR, we present a parameter learning method using particle swarm optimization (PSO) and the least squares method, and use it to predict the trend of the epidemic. Aiming at the social network data, we provide a specific method to realize sentiment analysis during the epidemic and propose an explainable fake news detection technique based on a variety of data mining methods.