Multi-layer Heterogeneous Network Research Articles

The NEDC-GTOPSIS Node Influence Evaluation Algorithm Based on Multi-Layer Heterogeneous Classroom Networks

To address the deficiency in the analysis of individual students within existing research on in-classroom social networks and the constraints of traditional centrality metrics in identifying influential nodes, this paper presents the NEDC-GTOPSIS evaluation method for evaluating node influence in multi-layer heterogeneous networks. Initially, students' friendship, interaction, and attribute information are leveraged to compute neighborhood overlap and attribute similarity between nodes, to construct the Composite Relationship Network. Subsequently, the Seat Similarity Network is constructed by applying the Nearest-Neighbor Effective Distance Criterion to compute seat similarity across various class sessions among nodes. Finally, the structure characteristics of two networks serve as influence decision indicators, and the GRA-TOPSIS algorithm, based on the combined weight method, evaluates nodes' influence. Experiments demonstrate that, compared to traditional single-layer relational networks and classical algorithms, this method can more effectively assess influential student nodes.

A new important nodes identification method in multi-layer heterogeneous combat network with meta-path centrality

Abstract Identifying important nodes is of great significance to improving the stability and security of heterogeneous combat networks. Due to the heterogeneity of nodes and the diversity of connections, heterogeneous combat networks usually are multi-layered. In order to model the combat network more accurately and identify important nodes, this paper proposes a new important nodes identification method in multi-layer heterogeneous combat network (MHCN). This method takes into account not only the topological information between nodes, but also the meta-paths formed by node interactions and the closeness of their associations. Furthermore, it considers the uncertainty of the command and control (C2) structure within MHCN. Specifically, first, tensor representation of MHCN is proposed to represent the intra-layer network and inter-layer network between nodes. Then, meta-path and its calculation method are proposed to capture interaction information between nodes. Next, intra-layer degree centrality, meta-path centrality, combined importance of node and C2 structure entropy are proposed to identify important nodes in MHCN, which can use the interaction characteristics of intra-layer and inter-layer to measure node importance in MHCNs with different C2 structures. Finally, experiments are carried out on real combat network case to verify the effectiveness and practicality of the proposed method. The results provide useful insights for operational guidance and the design of C2 structure.

Modeling the Coupling Propagation of Information, Behavior, and Disease in Multilayer Heterogeneous Networks

Modeling the Coupling Propagation of Information, Behavior, and Disease in Multilayer Heterogeneous Networks

A Stochastic Geometry-Based Cost-Efficient Caching Strategy in Device-to-Device-Enabled Multi-Layer Heterogeneous Networks

A Stochastic Geometry-Based Cost-Efficient Caching Strategy in Device-to-Device-Enabled Multi-Layer Heterogeneous Networks

Finite-time quasi-synchronization of multi-layer heterogeneous networks with distributed hybrid control

In this paper, the finite-time quasi-synchronization of a multi-layer heterogeneous network with a leader node is studied. A novel distributed control approach that incorporates impulsive control is introduced to achieve finite-time quasi-synchronization of the whole network. The control protocol is distributed, the impulses are synchronized, and the maximum time interval of impulses is bounded. Within this framework and based on the maximum impulsive interval, a finite-time quasi-synchronization criterion for the multi-layer heterogeneous network is established. A close relationship between the settling time and the maximum impulsive interval is discovered. Simulations are shown for illustration and verification.

A product requirement development method based on multi-layer heterogeneous networks

The rapid advancement of emerging technologies in the information age has resulted in intricate, diverse, and rapidly changing user requirements, thereby reducing the effectiveness of traditional requirement development approaches. This paper proposes a multi-layer heterogeneous network-based method for product requirement development to identify key requirements in the product development process and address the complex relationships among users, requirements, products, and requirement development teams. By incorporating the theory of hesitant fuzzy into the expert decision-making process, this method constructs four single-layer networks: user attention network, expert trust network, requirement correlation network, and component combination network, which assigns realistic significance to nodes in each network layer and establishes connections between different networks, forming a multi-layer heterogeneous network model for product requirement development. Furthermore, this paper defines indicators such as layer significance and node comprehensive significance and proposes a calculation method for node comprehensive significance based on biased random walk, which enables deep integration of node’s realistic significance and topological centrality. The proposed method is applied to a data experiment of product requirement development for a specific type of new energy vehicle. By analyzing the calculation results of node comprehensive significance, high-quality recommendations are provided for the development team from the perspectives of functional requirements, product components, and user groups. Moreover, comparative experiments to assess algorithm performance demonstrate that the average accuracy of the proposed algorithm is 0.95, which is significantly higher than that of the commonly used single-layer network node centrality measurement methods and recently proposed multi-layer network node centrality measurement methods. These results reflect the advancement and applicability of the proposed method in product development scenarios.

The node importance evaluation method based on graph convolution in multilayer heterogeneous networks

ABSTRACT Node importance evaluation is a hot issue in complex network analysis. Existing node importance evaluation methods are mainly oriented to homogeneous networks, which ignore the heterogeneity of node types and edges. We propose an MLN critical node evaluation method based on graph convolution. In this paper, we generate the feature matrix of nodes. Considering the diversity of node types in the network, we design an adapted node sampling method based on the meta path. An MLN node embedding model is constructed based on a graph convolutional network (MGC). Besides, the negative sampling technique is used to complete MGC training. Metrics of critical node evaluation are constructed by combining the node embedding vectors and local structural features to evaluate the node's importance. The experimental results show that the proposed method has better evaluation accuracy than the K-Shell algorithm (K-Shell), K-shell-based gravity model ranking algorithm (KSDG), the Page Rank algorithm in MLN (PR), influence maximization based on network embedding (IMNE) and the node ranking algorithm based on information entropy (ERM).

Constructing a multi-layer heterogeneous networks model to explore the public opinion evolution pattern of key users in public health emergencies

This article aims to discover key users in public health emergencies and explore their public opinion evolution patterns, thus providing theoretical support for the establishment of a clear cyberspace. In this article, a multi-layer heterogeneous network model based on multiple node attributes (user nodes, microblogs nodes, emotional nodes and topic nodes) is constructed at each stage of the public opinion cycle. Specifically, a novel semi-supervised self-training method based on the bidirectional encoder representations from transformers (SSST-BERT) method is proposed to automatically label the fine-grained emotional nodes. The latent Dirichlet allocation (LDA) model is used to construct the topic nodes. Moreover, the degree centrality, betweenness centrality and closeness centrality of the constructed heterogeneous network are adopted to dynamically identify key users. Finally, the emotional states and topic tendencies of key users are explored to obtain the public opinion evolution pattern of emergencies. The experimental results show that the SSST-BERT automatically labels emotion categories with an F1-score of 80.48%. The key users identified by the constructed heterogeneous network are more representative of the opinion status of ordinary users. Analysis of the public opinion status of key users reveals that netizens show more negative emotions such as anger and fear in public health emergencies, and the shift of focus drives the evolution of discussion topics.

Discussion of “Risk Propagation in Multilayer Heterogeneous Network of Coupled System of Large Engineering Project”

Discussion of “Risk Propagation in Multilayer Heterogeneous Network of Coupled System of Large Engineering Project”

RESEARCH OF FUNCTIONAL PARAMETERS OF INFOCOMMUNICATION NETWORKS 6G

The work presents the results of research into 6G technology, which will allow integrating all types of access in one mobile phone, ensuring smooth switching between services. New options for the use of mobile networks, such as sensing and artificial intelligence, are considered, which form new indicators of the quality of the information service – the resolution of sensing and the probability of output. For new applications, the prospects for further research into related indicators, such as flexibility and scalability to support own artificial intelligence services and network reliability level, are considered. Prospects for the development of electronic health care based on mobile communication systems with increased requirements for reliability, availability, security and confidentiality are considered. A platform for aerial mobility of various high-resolution sensors and cameras for various industries based on unmanned aerial vehicles has been investigated. The fundamental energy limitations of calculations related to information processing, which are an important step on the way to the successful deployment of 6G wireless networks, are considered. In the design of next-generation wireless communication networks, it has been determined that traditional AI optimization algorithms (such as federated learning) usually consider the bandwidth or delay of wireless connections as a weight for distributed multiprocessor data exchange, without considering the power energy bounds between different devices in different regions. This ambiguous consideration of AI energy limitations or power costs may lead to a large divergence between the design of a wireless network and the actual deployment of AI in the future. For this reason, equal importance must be attached to green AI and green communications. Therefore, when designing the architecture of the 6G information communication system, it is necessary to fully consider the impact of artificial intelligence models, algorithms and equipment on energy consumption to provide economic benefits to customers with corresponding operating costs of the system. The peculiarities of providing users with access to Internet services at any time through the same device regardless of their location, which is one of the ultimate goals of creating effective wireless networks, are discussed. The proposed concept of green AI called Oncefor-All (once and forever), according to which it is proposed to train an information communication network with further specialization during deployment will allow for efficient logical implementation on many devices, taking into account the given resource constraints. At the same time, terrestrial and non-terrestrial 6G networks will be fully integrated at the system level, ensuring the convergence of services, radio interfaces, networks and user devices. By organically combining these two access environments into one converged multi-layer heterogeneous network covering the entire globe, 6G technology will provide users with the same service. Ensuring the global delivery of mobile services will be an important aspect of the development of the 6G network.

3 Dimension Multilayer Heterogenous Ultra Dense Networks

The development of communication networks poses new challenges in the field of scientific research. At the same time, one of the main directions of development is the creation of highdensity and ultra-dense networks. Ultra-dense networks already belong to the technologies of communication networks of the sixth generation and the requirements for them are formed in the conditions of their deployment in three-dimensional space. Starting with the construction of fifth generation communication networks, communication networks are considered as heterogeneous, in which various technologies can be used together in the process of providing network services, for example, the Internet of Things, unmanned aerial vehicles, vehicular ad hoc networks, etc. This leads to the need to define and study three-dimensional multi-layer heterogeneous ultra-dense networks, which is the subject of this article.

Identification of Important Nodes in Multilayer Heterogeneous Networks Incorporating Multirelational Information

Centrality is an effective method to identify important nodes in complex networks, but it is still a challenge to find influential nodes by making full use of multiple relationships and global network topological features in complex networks. To address these problems, this article proposes an importance identification method for multilayer heterogeneous network node by incorporating multirelational information (MLC). This method studies the relational characteristics of heterogeneous nodes in detail and divides the heterogeneous nodes into different layers according to the node types, which can be further divided into core and auxiliary layers. The importance of the auxiliary layer is quantified by designing the interlayer influence and determining the interlayer influence weights of different connectivity influences; the centrality score of heterogeneous nodes under multiconnectivity relationships is fused using the transmission characteristics of internode relationships in the auxiliary layer, which in turn measures the importance of nodes in the core layer. To evaluate the proposed algorithm, we conduct experiments on five real multilayer heterogeneous networks of different sizes. The results show that MLC can make full use of different types of internode association relationship information, effectively fuse network structure information such as the neighbor weights of core and auxiliary layer nodes, and outperform the existing techniques in identifying important nodes.

AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks

Adept network management is key for supporting extremely heterogeneous applications with stringent quality of service (QoS) requirements; this is more so when envisioning the complex and ultra-dense 6G mobile heterogeneous network (HetNet). From both the environmental and economical perspectives, non-homogeneous QoS demands obstruct the minimization of the energy footprints and operational costs of the envisioned robust networks. As such, network intelligentization is expected to play an essential role in the realization of such sophisticated aims. The fusion of artificial intelligence (AI) and mobile networks will allow for the dynamic and automatic configuration of network functionalities. Machine learning (ML), one of the backbones of AI, will be instrumental in forecasting changes in network loads and resource utilization, estimating channel conditions, optimizing network slicing, and enhancing security and encryption. However, it is well known that ML tasks themselves incur massive computational burdens and energy costs. To overcome such obstacles, we propose a novel layer-based HetNet architecture which optimally distributes tasks associated with different ML approaches across network layers and entities; such a HetNet boasts multiple access schemes as well as device-to-device (D2D) communications to enhance energy efficiency via collaborative learning and communications.

BRANEnet: embedding multilayer networks for omics data integration

BackgroundGene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANEnet is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism.ResultsWe test BRANEnet on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 min. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANEnet is compared to existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks.

A Secure and Intelligent Framework for Vehicle Health Monitoring Exploiting Big-Data Analytics

The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, efficient, flexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth. For this, we proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective owner-driver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, we unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition’s outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.

Risk Propagation in Multilayer Heterogeneous Network of Coupled System of Large Engineering Project

Because of the long duration, multiplicity of technical disciplines, large number of project stakeholders, and high levels of complexity and uncertainty, project risk propagation control in large engineering projects (LEPs) is an enormous challenge for project managers. Although previous research has attained many risk propagation achievements regarding complex systems, complex coupled system modeling ignores the heterogeneity of the organizational structure of the actual LEPs, which affects the reliability of the calculated risk propagation results. To bridge this gap, this paper abstracts the LEP structure into a multilayer heterogeneous network comprising the stakeholder network and the project schedule network and proposes a method for characterizing the coupling relationship between two layers of the heterogeneous network. Then, the multiple uncertainties in risk propagation are greatly considered, and a risk propagation model is established based on the multilayer heterogeneous network and improved related schedule risk analysis model (CSRAM). Finally, the proposed model is applied to determine the delayed payment risk propagation in an actual LEP to verify the feasibility of the proposed model. The results indicate the following: (1) the delayed payment risk of a stakeholder evolves into a delay in the entire project; (2) several groups of comparative simulation experiments show that the proposed model, which considers multiple uncertainties and actual networks, includes more comprehensive and valuable risk information; (3) the multiple uncertainties of risk propagation are gradually superimposed with the increase in the number of construction activities affected by risk propagation; and (4) controlling for risk factors that have a high degree of influence and a large negative impact is an effective measure for blocking risk propagation across multilayer networks. This research lays an important foundation for risk propagation control in LEPs and contributes to the extension of the current theory of risk propagation in complex systems.

5G multi-tier radio access network planning based on voronoi diagram

Network planning of multi-layer heterogeneous mobile networks with complex topology is an important task. In this paper the spectral and energy efficiency of integrated LTE/Wi-Fi technologies for 5G are improved. A method of adaptive formation of the structure of radio access level (RAL) with the provision of the required quality of service (QoS) and the possibility of broadband data transmission is proposed. The Voronoi tessellation is used for designing the RAL of 5G mobile networks for the placement of base stations, which allowed optimum delimiting the coverage area for each base station and provide users the cell interface services. To minimize interference there was proposed a method of dynamic frequency reuse for different sizes of Voronoi cells. Modelling shows the developed method is effective both at low and at high network load, however, at high load there is a slightly smaller gain in energy efficiency than at low load.

A Formal Representation of the Semantics of Structural Geological Models

A structural geological model describes the structure of subsurface and plays an important role in the exploration of mineral and petroleum resources. Despite the widespread use of three-dimensional geological models, the theoretical research of informatics in the field of structural geology is still very limited. We have noticed a lack of methods for integrating explicit semantics of field observation data and geophysical data into geological models. The existing model representation methods focus on accurately representing the geometric morphological information of underground structures, ignoring the high-level semantics implied in the model. The formal representation of the semantic information is necessary to promote the development of intelligent methods in geomodeling and geophysical inversion. In this paper, we propose a new framework to formally represent the semantics of structural geological models with a clear distinction of geometric and geological semantics. For the geometric semantics, based on the extension of the 9-intersection model, we mathematically define the spatial topological relations between geometric objects that make up the geological model. For the geological semantics, we define the geological contact and compositional relations between geological bodies and geological surfaces and reveal the temporal implications of these geological relationships. We design a multilayer heterogeneous network as a computer characterization of the semantics of the geological model. A better representation of semantic information aids in the creation and validation of geological models, as well as management, queries, and analyses of geological knowledge.

Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction.

Abnormal miRNA functions are widely involved in many diseases recorded in the database of experimentally supported human miRNA-disease associations (HMDD). Some of the associations are complicated: There can be up to five heterogeneous association types of miRNA with the same disease, including genetics type, epigenetics type, circulating miRNAs type, miRNA tissue expression type and miRNA-target interaction type. When one type of association is known for an miRNA-disease pair, it is important to predict any other types of the association for a better understanding of the disease mechanism. It is even more important to reveal associations for currently unassociated miRNAs and diseases. Methods have been recently proposed to make predictions on the association types of miRNA-disease pairs through restricted Boltzman machines, label propagation theories and tensor completion algorithms. None of them has exploited the non-linear characteristics in the miRNA-disease association network to improve the performance. We propose to use attributed multi-layer heterogeneous network embedding to learn the latent representations of miRNAs and diseases from each association type and then to predict the existence of the association type for all the miRNA-disease pairs. The performance of our method is compared with two newest methods via 10-fold cross-validation on the database HMDD v3.2 to demonstrate the superior prediction achieved by our method under different settings. Moreover, our real predictions made beyond the HMDD database can be all validated by NCBI literatures, confirming that our method is capable of accurately predicting new associations of miRNAs with diseases and their association types as well.

Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA-Disease Association Prediction.

Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the medical field. Here, based on heterogeneous networks built on the information of known miRNA–disease associations, miRNA function similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, we developed a computing model of biased random walk with restart on multilayer heterogeneous networks for miRNA–disease association prediction (BRWRMHMDA) through enforcing degree-based biased random walk with restart (BRWR). Assessment results reflected that an AUC of 0.8310 was gained in local leave-one-out cross-validation (LOOCV), which proved the calculation algorithm’s good performance. Besides, we carried out BRWRMHMDA to prioritize candidate miRNAs for esophageal neoplasms based on HMDD v2.0. We further prioritize candidate miRNAs for breast neoplasms based on HMDD v1.0. The local LOOCV results and performance analysis of the case study all showed that the proposed model has good and stable performance.