Global Network Information Research Articles

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

BackgroundCardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI.MethodsThe aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity.ResultsA total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI.ConclusionsAnalysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

Enhancing network robustness with structural prior and evolutionary techniques

Robustness optimization in complex networks is a critical research area due to its implications for the reliability and stability of various systems. However, existing algorithms encounter two key challenges: the lack of integration of prior network knowledge, leading to suboptimal solutions, and high computational costs, which hinder their practical application. To address these challenges, this paper introduces Eff-R-Net, an efficient evolutionary algorithm framework aimed at enhancing the robustness of complex networks through accelerated evolution. Eff-R-Net leverages global and local network information, featuring a novel three-part composite crossover operator. Prior network knowledge is incorporated in mutation and local search operators to expedite the construction of networks with superior robustness. Additionally, a simplified method for calculating robustness enhances efficiency, while adaptive hyper-parameters dynamically adjust operators execution probabilities for optimal evolution. Extensive evaluations on both synthetic (Scale-Free, Erdös-Rényi, and Small-World) and three infrastructure real-world networks demonstrate the superiority of Eff-R-Net. The algorithm improves robustness by 12.8% and reduces computational time by 25.4% compared to state-of-the-art algorithm in real-world network experiments. These findings underscore Eff-R-Net's versatility and potential in enhancing network robustness across different domains.

A novel method for identifying key nodes in multi-layer networks based on dynamic influence range and community importance

Identifying key nodes in multi-layer networks is a hot research topic in complex network science and has broad application prospects, such as in mining enterprises that significantly affecting multi-layer industrial chains. Unlike single-layer networks, nodes in multi-layer networks exhibit heterogeneity due to varying connections and locations. There are also correlations between different network layers, which is particularly evident in industrial chains where companies operate across multiple layers of production, supply and distribution. It is necessary to consider the impact of these layers on the global performance of key node identification. In addition, due to changes in connections, the community structure of each network layer should be different, reflecting the dynamic nature of industrial collaborations and partnerships. However, existing research lacks the model that addresses the above problems. Therefore, this paper proposes a key node identification method based on Dynamic Influence Range and Community Importance (DIRCI), using both local and global information of the multi-layer network simultaneously. DIRCI determines the importance of nodes through three centrality measures: dynamic influence range-based centrality, network layer centrality and community-based centrality. Dynamic influence range-based centrality models node heterogeneity by combining the influence range of nodes and their neighbors with lower computational costs. Network layer centrality captures the corresponding importance for different network layers. Community-based centrality comprehensively considers the importance of community, the importance of each node within the community and between different communities. Experimental results for nineteen multi-layer networks show that DIRCI achieves better performance of key node identification than the latest algorithms.

An online integrated satellite–terrestrial IoT task offloading and service deployment strategy

As a core component of 6G networking, low earth orbit (LEO) satellite networks are considered a promising solution for providing network services in remote areas and have received considerable attention in recent years. Meanwhile, with the rapid development of IoT terminals and applications, an increasing emphasis has been placed on task offloading for real-time tasks. However, most existing works on task offloading for integrated satellite–terrestrial IoT (IST-IoT) applications are based on the assumption that the global task and network information is known, an assumption that is obviously not met in online real-time task offloading. In addition, current studies rarely consider the tradeoff among the task offloading volume, satellite endurance and wholesale cost of terrestrial resources, which are precisely the holistic issues that satellite network operators must confront. To address such issues, this study considers task offloading, service deployment and resource allocation in highly dynamic online task offloading scenarios with the goal of maximizing the average task offloading volume. Moreover, virtual queues are constructed in this study to account for the impacts of long-term satellite endurance and terrestrial network resource costs on task offloading. We first decouple the original problem to obtain a single-time-slot optimization problem within the Lyapunov optimization framework, and we then propose a 2-stage optimization approach based on deep reinforcement learning (DRL) to solve the complex single-time-slot mixed integer nonlinear programming (MINLP) problem. Simulation experiments demonstrate that our proposed algorithm can yield near-optimal solutions that show superior performance compared to existing results.

A hybrid congestion control scheme for Named Data Networking via SDN

As the mainstream congestion control in Named Data Networking (NDN), the hybrid control mechanism integrates the advantages of consumer and router control mechanisms. However, The concurrent control by both consumers and routers may results in degraded data transmission performance. Therefore, we propose a novel hybrid congestion control scheme for NDN, called ndnSieve. By using the characteristics of Software Defined Network (SDN) that can achieve global network information acquisition as well as global resource scheduling, ndnSieve manages to invoke consumer and router control mechanisms based on different congestion states in the network respectively and achieves the synergy between two kinds of mechanisms. The results of experiment demonstrate that, compared with existing works, ndnSieve has the lowest average round-trip time, relatively high level of throughput and fairness, effectively improving the Quality of Service (QoS) of the network.

MUSCAT: Distributed multi-agent Q-learning-based minimum span channel allocation technique for UAV-enabled wireless networks

We consider a minimum span channel allocation problem (MS-CAP) to overcome spectrum scarcity and facilitate the efficiency of unmanned aerial vehicle (UAV)-enabled wireless networks. Basically, the MS-CAP minimizes the difference between the maximum and minimum used frequency, i.e., the required total bandwidth, while guaranteeing the quality-of-service (QoS) requirements for each wireless link in the network. The conventional optimal minimum span channel allocation (MS-CA) scheme is based on a centralized approach, assuming that global network information is available at the central controller. In practice, however, this may not be feasible for dynamic environments like UAV-enabled wireless networks since the real-time exchange of network information and channel allocation results with dynamically moving UAVs is formidable. Hence, we propose a novel practical MS-CA algorithm based on distributed multi-agent reinforcement learning (MARL), where each agent independently learns its best strategy with its local observations. To the best of our knowledge, the proposed technique is the first work of designing a distributed MARL for the MS-CAP for multi-UAV-enabled wireless networks in the literature. Numerical results reveal that the proposed distributed MS-CA technique can efficiently save the required total bandwidth while ensuring the QoS requirements of each link, represented by the signal-to-interference plus noise ratio (SINR) threshold, even in dynamic wireless networks. It validates the applicability of the proposed distributed MS-CA framework to dynamic networks.

Learning-Based Hierarchical Decision-Making Framework for Automatic Driving in Incompletely Connected Traffic Scenarios.

The decision-making algorithm serves as a fundamental component for advancing the level of autonomous driving. The end-to-end decision-making algorithm has a strong ability to process the original data, but it has grave uncertainty. However, other learning-based decision-making algorithms rely heavily on ideal state information and are entirely unsuitable for autonomous driving tasks in real-world scenarios with incomplete global information. Addressing this research gap, this paper proposes a stable hierarchical decision-making framework with images as the input. The first step of the framework is a model-based data encoder that converts the input image data into a fixed universal data format. Next is a state machine based on a time series Graph Convolutional Network (GCN), which is used to classify the current driving state. Finally, according to the state's classification, the corresponding rule-based algorithm is selected for action generation. Through verification, the algorithm demonstrates the ability to perform autonomous driving tasks in different traffic scenarios without relying on global network information. Comparative experiments further confirm the effectiveness of the hierarchical framework, model-based image data encoder, and time series GCN.

Non-negative scaled edge-consensus of saturated networked systems via adaptive output-feedback control

This paper investigates fully distributed scaled non-negative edge-consensus problems of networked systems with actuator saturation and unmeasurable internal states. By using the adaptive control method, output-feedback-based low-gain technique, and graph theory, a new adaptive algorithm is designed to obtain scaled edge-consensus conditions, under which the difficulties caused by the constraints on edge states and controllers are overcome. There are three interesting characteristics that any global information of networks is not used in this paper’s algorithm and result, including the number of vertexes and edges; the feasible solutions of the scaled edge-consensus conditions exist and can be easily obtained; the convergence rate of the controller is adjustable. Furthermore, the designed algorithm is expanded in the cases without actuator saturation. Finally, three examples are given to verify the theoretical results.

A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.

SO‐CPP: Sailfish optimization‐based controller placement in IoT‐enabled software‐defined wireless sensor networks

SummaryOne of the expanding network topologies that is frequently utilized to improve network development by successfully separating the control plane and data plane is software‐defined networking (SDN). In order to function inside complex sensor networks, the SDWSN system frequently relies on centralized controller logic that pulls global network information. In wireless sensor networks (WSNs), using several SDN controllers is known as a promising strategy due to reliability and performance considerations. However, using numerous controllers increases the synchronization overhead between the controllers. Consequently, it is a difficult research challenge to discover the best placement of SDN controllers to enhance the performance of a WSN, subject to the maximum number of controllers calculated based on the synchronization overhead. This research introduces a novel technique to overcome the controller placement problem (CPP) by optimizing multi‐constraints within the sensor networks. For selecting the optimal controllers and placing them in an optimal location, a novel sailfish optimization (SO) strategy is introduced that can enhance the search space and maintain optimal global values throughout the iteration. Then, node clustering is performed using the fuzzy‐C‐means (FCM) clustering technique, which can reduce energy consumption and path delay within the network. The overall latency obtained by the proposed method is about 0.51 and 0.56 ms, and a total run time of 4 ms for both single sink and multi‐sink, respectively. The proposed method is implemented in the MATLAB platform, and different performance metrics are analyzed and compared with existing techniques.

LCGNet: Local Sequential Feature Coupling Global Representation Learning for Functional Connectivity Network Analysis with fMRI.

Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learning techniques, such as convolutional neural networks (CNNs), have been used to learn high-level feature representations of FCNs for automated brain disease classification. Even though convolution operations in CNNs are good at extracting local properties of FCNs, they generally cannot well capture global temporal representations of FCNs. Recently, the transformer technique has demonstrated remarkable performance in various tasks, which is attributed to its effective self-attention mechanism in capturing the global temporal feature representations. However, it cannot effectively model the local network characteristics of FCNs. To this end, in this paper, we propose a novel network structure for Local sequential feature Coupling Global representation learning (LCGNet) to take advantage of convolutional operations and self-attention mechanisms for enhanced FCN representation learning. Specifically, we first build a dynamic FCN for each subject using an overlapped sliding window approach. We then construct three sequential components (i.e., edge-to-vertex layer, vertex-to-network layer, and network-to-temporality layer) with a dual backbone branch of CNN and transformer to extract and couple from local to global topological information of brain networks. Experimental results on two real datasets (i.e., ADNI and ADHD-200) with rs-fMRI data show the superiority of our LCGNet.

Fully distributed consensus of linear multi-agent systems via dynamic event-triggered control

This paper investigates the fully distributed consensus control problem for general linear multi-agent systems via dynamic event-triggered scheme. To address the need to reduce network communication burdens and avoid involving any global features of multi-agent networks, a novel fully distributed controller based on the open-loop state estimator of agents is presented. To improve anti-interference ability and practicability of the proposed approach, an event-triggered strategy which can guarantee a precise lower bound of triggering intervals for each follower is designed with a virtual internal variable. Then, the stability of the multi-agent systems is analyzed without any global network information. Several simulation experiments are conducted to illustrate theoretical results.

A fully distributed event-Triggered control approach to leader-following consensus of multiagent systems

This paper investigates the leader-following consensus problem for general linear multiagent systems under a fully distributed manner. To avoid using the global information of the communication network, two node-based adaptive parameters are designed and introduced to the fully distributed event-based control law. Such a protocol is more scalable and flexible for large-scaled and complex networked system, such as microgrids or multiple unmanned vehicles. A novel event-triggered rule is presented, and an internal dynamic variable is introduced in it to further cut down the triggering times and save the communication resources. A well-designed Lyapunov function candidate is constructed to not only ensure the convergence of the close-looped systems but also avoid using the eigenvalue of Laplacian matrix in the dynamic variable. Moreover, it is proved that the designed event-triggered conditions can avoid the Zeno behaviour. Finally, a simulation example based on a group of unmanned intelligent vehicle systems is provided to demonstrate the validity of the designed control method.

Fully Distributed Dynamic Event-Triggered Bipartite Formation Tracking for Multiagent Systems With Multiple Nonautonomous Leaders.

Considering that cooperative interactions and antagonistic interactions between neighboring agents may exist simultaneously in practice, this article studies the bipartite time-varying output formation tracking (BTVOFT) problems for homogeneous/heterogeneous multiagent systems with multiple nonautonomous leaders under switching communication networks. First, a full-dimensional observer-based nonsmooth distributed dynamic event-triggered (DDET) output feedback control scheme is proposed to ensure that BTVOFT is achieved, and the Zeno behavior is excluded. Note that the nonsmooth distributed control scheme requires global communication network information and may cause unexpected chattering effect, and the design cost of full-dimensional observer is relatively high. Thus, a reduced-dimensional observer-based continuous fully DDET scheme is proposed. Compared with the existing event-triggered schemes, the dynamic event-triggered scheme can ensure larger interevent times by introducing an additional internal dynamic variable. Finally, the effectiveness and performance of the theoretical results are validated by numerical simulations.

CDBRA: Community Detection Based on Random Algorithm in Social Networks

Understanding the topology and functions of complex networks allows us to derive valuable information from them. There are various types of these networks. Community detection is a significant research area that involves dividing a network graph into subsets of nodes, known as communities. Each community consists of nodes that have dense communication with each other and sparse communication with nodes outside the community. This work proposes the use of Community Detection based on random Algorithm (CDBRA) to identify novel communities with low complexity and high accuracy by using both local and global network information. The proposed method consists of four components: Pre-Processing, Node Identification, Intra-Community Structure, and Inter-Community Structure. In the initial component, the task involves recognizing and saving similarity measures. Additionally, it requires assigning suitable weights to network vertex and edges, taking into the account of local and global network information. The next level involves using a random algorithm enhanced by nodes' weights to determine similarity measures for Node Identification. The third level, Intra-Community Structure, aims to achieve various community structures. The fourth level ultimately chooses the optimal community structure by taking into account the Inter-Community Structure and the evaluation functions derived from network’s local and global information. To assess the proposed method on various scenarios involving real and artificial networks. The proposed method outperforms existing methods in detecting community structures similar to real communities and provides efficient evaluation functions for all types and sizes of networks.

Distributed Adaptive Event-Triggered TVGF for Generic Linear Multi-Agent Systems Under Directed Graph

This paper investigates event-triggered time-varying grouping formation (TVGF) problem for generic linear homogeneous multi-agent systems under directed graphs. A distributed adaptive event-triggered TVGF algorithm is proposed to ensure that the goal of TVGF can be achieved and the communication frequency between agents is reduced. Compared with previous formation control methods, the algorithm presented in this paper can complete the group formation task while saving communication energy. In addition, the algorithm’s dependence on global network information is also eliminated by using adaptive techniques. Finally, the availability of the proposed control law is established by numerical simulation.

Metabolite-disease interaction prediction based on logistic matrix factorization and local neighborhood constraints.

Increasing evidence indicates that metabolites are closely related to human diseases. Identifying disease-related metabolites is especially important for the diagnosis and treatment of disease. Previous works have mainly focused on the global topological information of metabolite and disease similarity networks. However, the local tiny structure of metabolites and diseases may have been ignored, leading to insufficiency and inaccuracy in the latent metabolite-disease interaction mining. To solve the aforementioned problem, we propose a novel metabolite-disease interaction prediction method with logical matrix factorization and local nearest neighbor constraints (LMFLNC). First, the algorithm constructs metabolite-metabolite and disease-disease similarity networks by integrating multi-source heterogeneous microbiome data. Then, the local spectral matrices based on these two networks are established and used as the input of the model, together with the known metabolite-disease interaction network. Finally, the probability of metabolite-disease interaction is calculated according to the learned latent representations of metabolites and diseases. Extensive experiments on the metabolite-disease interaction data were conducted. The results show that the proposed LMFLNC method outperformed the second-best algorithm by 5.28 and 5.61% in the AUPR and F1, respectively. The LMFLNC method also exhibited several potential metabolite-disease interactions, such as "Cortisol" (HMDB0000063), relating to "21-Hydroxylase deficiency," and "3-Hydroxybutyric acid" (HMDB0000011) and "Acetoacetic acid" (HMDB0000060), both relating to "3-Hydroxy-3-methylglutaryl-CoA lyase deficiency." The proposed LMFLNC method can well preserve the geometrical structure of original data and can thus effectively predict the underlying associations between metabolites and diseases. The experimental results show its effectiveness in metabolite-disease interaction prediction.

An effective heterogeneous information network representation learning framework

Given the heterogeneity of real-world networks and the low efficiency of directly mining networks, heterogeneous information network (HIN) representation learning, which learns low-dimensional embeddings of nodes to represent various structural and semantic information in HINs, becomes a very crucial topic. Existing HIN based representation learning methods either omit the global information of networks, or only consider the first-order neighbors of each node. The two problems reflect the insufficient extraction and exploitation of network information for HIN embedding. To address these problems, we propose a novel Heterogeneous Graph SAmple and aggreGatE framework, named HGSAGE, for learning low-dimensional similarity-preserved embeddings of nodes in HINs. This framework consists of three mechanisms, i.e., the gravity model based on the meta-path reachable graphs mechanism to capture the global information of HINs, the node-level sampling and aggregating mechanism to sample and incorporate features from immediate and mediate neighbors of nodes, and the semantic-level aggregating mechanism to combine embeddings with respect to different meta-paths. Extensive experiments on three real-world heterogeneous networks of different types and scales for multiple tasks show that the proposed framework significantly outperforms the baselines. Moreover, HGSAGE has important application values in this research field.

Artificial Intelligence Empowered Traffic Control for Internet of Things with Mobile Edge Computing

Mobile edge computing (MEC) is one of the efficient technologies to provide satisfying quality of experience (QoE) for emerging computation-intensive applications in internet of things (IoT). However, some new challenges will be encountered when MEC is applied in a large-scale IoT with massive access devices or heavy traffic loads such as load balancing and traffic offloading. Aiming at the solution of these problems, this paper proposes a learning-based traffic control architecture for IoT with MEC. Moreover, a deep-learning-based load balancing framework is developed to control user association in IoT. The user association is determined at each IoT access points (IAP) by the deep neural network (DNN), which is the duplication of the well-trained DNN with the global network information. In addition, we propose a reinforcement-learning-based partial traffic offloading scheme to reduce the traffic origination. The IoT devices are able to independently decide its offloading radio according to the channel quality information, service requirement, and workload of the IAP. Simulation results indicate that the proposed deep-learning-based load balancing scheme is able to achieve uniform traffic distribution, and meanwhile our partial offloading policy can significantly reduce the network traffic.

Model-Independent Robust Consensus of Multiple Euler–Lagrange Systems

The robustness of multiple Euler–Lagrange systems (MELSs) measures the capability of maintaining coordination control performance when experiencing disturbance or faults. This article investigates a leader–follower robust consensus problem of MELSs, which experience time-varying input disturbance and uncertain communication link faults (CLFs). First of all, based on an adaptive control theory, we design fully distributed observers for estimating the dynamic and state of leader, which have robustness to CLFs. Then, an observer-based proportional–integral (PI) control protocol is designed to achieve consensus of MELSs with robustness to time-varying input disturbance. Different from the existing related results, this robust observer-based PI controller is fully distributed and model independent, which is irrelevant to any prior information (i.e., the structures and features) of agent dynamic or global network information. At last, the validity of the proposed theoretical results is confirmed by a simulation example.