At present, most edge servers adopt popularity-based caching strategies, prioritizing the caching of content with the highest overall popularity on user-side edge servers. However, in social network scenarios, user interests and preferences are highly personalized and dynamically changing. This results in existing caching strategies often failing to adjust the cache placement of content in real time according to individual user preferences, leading to suboptimal edge cache hit rates, increased user request response latency, and a decline in quality of service (QoS) for the user experience. To address this issue, we propose a new caching strategy tailored for large-scale social content based on knowledge graph neural network (KGNNC). First, an entity-relation KG is constructed from users' triple data$(\boldsymbol{h}, \boldsymbol{r}, \boldsymbol{t})$on social platforms. Next, a graph convolutional neural network is employed to iteratively aggregate feature information from neighboring nodes and learn vector representations of the nodes. Finally, a reinforcement learning-based algorithm is utilized to determine the optimal caching location for content. Experimental results on multiple public datasets demonstrate, compared with several existing baseline algorithms (least recently used, least frequently used, neural network-based collaborative filtering, KG-DQN, and CAFR), the algorithm proposed in this article achieves a reduction in the average response latency of requests by 38.26%, 34.46%, 13.56%, 6.49%, 4.19% on MovieLens 1M dataset and 30.31%, 27.59%, 14.11%, 5.83%, 4.78% on last FM dataset, respectively. Meanwhile, experiment results demonstrate that the caching hit rate is increased by 24.2%, 25.1%, 14.4%, 6.5%, 3.53% on MovieLens 1M and 39.51%, 39.05%, 23.40%, 10.66%, 8.67% on last FM compared with the four existing baseline algorithms, respectively. These results verify the effectiveness of our algorithm in reducing response latency of user requests and improving caching hit rate of edge servers in social networks.

The evolution of 6G networks introduces heterogeneous services with stringent computing and latency demands. However, constrained edge resources, intricate task dependencies, and dynamic network fluctuations intensify resource contention, increasing the risk of node faults and service interruption. Current fault-tolerant methodologies lack the necessary adaptability to handle the coupled complexity of task interdependencies and volatile resource states, leading to sub-optimal decisions or excessive system overhead. To address these challenges, this paper innovatively proposes TransDiffuse—an intelligent preemptive service migration framework for 6G edge networks. First, the framework employs a Transformer-GAT hybrid model to capture long-range temporal load dynamics and spatial topological constraints, enabling accurate failure prediction. Second, to navigate the trade-off between migration overhead and service robustness, we devise a diffusion-based decision module. This module efficiently explores the discrete combinatorial solution space to synthesize near-optimal service orchestration. Furthermore, a comprehensive evaluation system is constructed to validate the effectiveness of TransDiffuse. Experiments demonstrate that TransDiffuse reduces energy consumption by 32.4%, decreases task completion time by 25.6%, and improves resource balance by 18.7%, while keeping service violations below 5%. This work achieves joint optimization of energy, delay, and resource efficiency, offering a robust solution for resilient service orchestration in 6G edge networks.

Digital twin (DT), as a key enabling technology for 6G edge intelligence, can establish real-time connections between digital twin objects and physical devices, ensuring real-time synchronization and thereby enhancing the service performance and stability of edge networks. This paper considers the high dynamics of edge networks and combines digital twins with edge networks to construct a three-layer network architecture. Based on the demands for low-latency services and system energy efficiency, we design a network metric: system overhead, to minimize service latency and system energy consumption. To achieve these system objectives, we integrate digital twin technology with multiagent deep reinforcement learning (MADRL), proposing a digital twin-driven multi-agent scheme for green and reliable resource allocation. This approach effectively minimizes system overhead and can adapt well to dynamic changes in terminal devices. Compared with baseline algorithms, it reduces system overhead by at least 7 % while maintaining significant reliable.

In the high-density data transmission and multi-terminal device environment of 6G edge networks, an efficient routing strategy is crucial. Existing routing methods lack adaptability in the face of network dynamics, which can result in service delays and connection interruptions. To address this issue, this paper proposes an innovative fault-tolerant traffic routing (FTTR) mechanism. Leveraging Message Passing Neural Networks (MPNNs) and Deep Q-Network (DQN), FTTR can deeply explore the interdependencies between links and make precise routing decisions. Extensive experiments demonstrate that FTTR mechanism achieves an average 27.72% increase in network load capacity compared to mainstream routing strategies. Its generalization and robustness significantly outperform Proximal Policy Optimization (PPO), clearly demonstrating its adaptability and stability to network dynamics.

Intelligent technology is driving the communication industry to a higher stage of autonomy. To enable the large-scale deployment of Autonomous Networks (ANs), We design a knowledge-driven self-healing dual-loop architecture throughout the fault lifecycle, Then we propose an optimization model that aims to minimize the loss of services operation. Simulations are conducted in a programmable network to validate the loop.

6G edge networks strive to offer ubiquitous intelligent services, requiring a greater emphasis on network stability and reliability. However, current networks present a low automation degree of the operation, administration and maintenance process. Consequently, active service migration away from abnormal network nodes and links, as well as automatic and transparent service recovery from sudden anomalies, become challenging tasks. These conditions underscore the urgency for an innovative service self-healing mechanism for 6G edge networks. Digital twin (DT) technology uses modeling to represent physical entities, thereby facilitating lifecycle management. However, the application of DT technology in networks is still a burgeoning field of study. In this paper, we explore the DT-driven service self-healing mechanism in 6G edge networks. Initially, we design a DT-based architecture for service self-healing. Subsequently, we construct a performance prediction mechanism leveraging graph neural networks (GNNs) to devise an efficient prediction model, which aims to accurately infer network performance and promptly detect abnormal network conditions. To maintain fine-grained service stability amidst potential network anomalies, we propose a DT-driven service redeployment mechanism enhanced by GNNs. Comprehensive experimental results reveal that our proposed mechanism can accurately predict flow-level delays and identify abnormal links and nodes. Furthermore, the DT-driven service redeployment mechanism effectively reduces service delay and enhances network load balance.

Network delay is a crucial indicator for realizing delay-sensitive task offloading, network management, and optimization in B5G/6G edge computing networks. However, the delay prediction for edge networks becomes complicated due to diverse access strategies and heterogeneous services’ storage, computing, and communication resource requirements. Current GNN-based delay prediction models such as RouteNet and PLNet lack the ability to express the complex associations between links and paths, so the predicted delay is not accurate. In this paper, we propose a novel end-to-end delay prediction model named MixerNet for edge computing, which is based on the mixed multi-layer perceptron (MLP). In this model, a mixed MLP architecture is applied to represent the association between links in the network topology and various paths. Observing that each link may have different effects on various paths, a weight matrix is then defined and multiplied by the path matrix to express it. Thus, a complete mapping frame from network characteristics (e.g., traffic intensity and routing schemes) to delay indicator is constructed. Finally, we perform extensive experiments on NSFNET and GEANT2 datasets and regard RouteNet as the baseline model. Experimental results show that MixerNet can accurately predict end-to-end delay results on various network topologies and the mean absolute error is merely about 0.36%. MixerNet also outperforms the baseline model in most evaluation indicators, especially the mean square error has a 3-fold decrease in NSFNET.

Knowledge graph completion (KGC) aims to perform link prediction to fill lost relations between entities by knowledge graph embedding (KGE). Translating embedding, as an efficient embedding method in KGE, is widely applied in numerous recent KGC models. However, these translating models may lack the ability to express various relation patterns and mapping properties for knowledge graphs (KGs). In this paper, a simple and well-performed translating model named TransC is proposed to express different relations. A multi-channel mechanism is defined firstly to constrain translating embeddings. Then a relation-aware transfer function is designed to break the expressive restriction and map triplets involving the same relation into a corresponding plane. We also mathematically prove that TransC is capable of expressing four popular relation patterns and all mapping properties. Finally, experimental results illustrate that TransC can efficiently represent the different relation patterns and properties and achieve better performance than state-of-the-art translating models.

MOTIVATION: Accurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene structure, regulation and function. Numerous efforts have been made to develop machine learning-based predictors for in silico identification of GSRs. However, it remains a great challenge to identify GSRs as the performance of most existing approaches is unsatisfactory. As such, it is highly desirable to develop more accurate computational methods for GSRs prediction. RESULTS: In this study, we propose a general deep learning framework termed DeepGenGrep, a general predictor for the systematic identification of multiple different GSRs from genomic DNA sequences. DeepGenGrep leverages the power of hybrid neural networks comprising a three-layer convolutional neural network and a two-layer long short-term memory to effectively learn useful feature representations from sequences. Benchmarking experiments demonstrate that DeepGenGrep outperforms several state-of-the-art approaches on identifying polyadenylation signals, translation initiation sites and splice sites across four eukaryotic species including Homo sapiens, Mus musculus, Bos taurus and Drosophila melanogaster. Overall, DeepGenGrep represents a useful tool for the high-throughput and cost-effective identification of potential GSRs in eukaryotic genomes. AVAILABILITY AND IMPLEMENTATION: The webserver and source code are freely available at http://bigdata.biocie.cn/deepgengrep/home and Github (https://github.com/wx-cie/DeepGenGrep/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Aiming at the problem that existing association rule mining algorithms cannot quickly mine faulty association rules in the current perception layer of electric power sensor networks, an improved eclat mining algorithm fast_eclat is proposed. The algorithm combines the characteristics of sparse data and large number of transactions at the perception layer of the power sensor network, and adopts a set intersection strategy based on pruning cross-counting, which reduces the computational complexity and improves the computational efficiency of the algorithm, which can more effectively deal with fault association rules. Comparative analysis through simulation experiments shows that the fast_eclat algorithm has better performance in the face of sparse data and large number of transactions.