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.

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.

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.

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.