Framework For Heterogeneous Networks Research Articles

Analysis of rumor spreading with different usage ranges in a multilingual environment

<p>This paper investigates rumor propagation in a multilingual environment, taking into account language usage variations. Firstly, a 2I2S2R model is proposed within a heterogeneous network framework that incorporates both immunologic and cross-transmitted mechanisms. Secondly, the paper calculates the basic reproduction number $ R_0 $ by the next-generation matrix method. Thirdly, the local asymptotic stability and the global asymptotic stability are further explored, which indicate that whether the rumor continuously spreads or becomes extinct is determined by the threshold. Finally, the numerical simulation and sensitivity analysis are given to illustrate the effectiveness of theoretical results and the influence of model parameters on rumor spreading.</p>

Unveiling hierarchical relationships for social image representation learning

Amidst the rapid propagation of images across social media platforms, the representation learning of social images has emerged as a prime research pursuit. A salient characteristic of social images lies in their multimodal properties, encompassing the visual content and textual descriptions present within individual images and the social relationships among different images. Notably, the content information within individual images and the structural information among different images exhibit a hierarchical nature. However, existing endeavors generally employ a flat framework, leading to suboptimal utilization of hierarchical relationships. Furthermore, the heterogeneity inherent in data content and structural information presents additional challenges in social image representation. In light of these challenges, we propose a novel Hierarchical Heterogeneous Graph Neural Network model for Social Image Representation learning, dubbed HHGSI. Our motivation lies in exploring and exploiting the hierarchical relationship between diverse modalities through designing the hierarchical heterogeneous network framework. HHGSI consists of an Intra-node Multimodal Graph Encoder and an Inter-node Heterogeneous Graph Neural Network to simultaneously capture fine-grained correlation within the image and heterogeneous relationships among the images. Moreover, a task-independent optimization objective is designed to make the model suitable for numerous network-oriented and multimodal tasks. Our proposal is extensively evaluated over four real-world datasets, and experimental results demonstrate the superiority of our proposal. Our code is publicly available in https://github.com/multimodal-code/HHGSI.

MEAHNE: miRNA-Disease Association Prediction Based on Semantic Information in a Heterogeneous Network.

Correct prediction of potential miRNA-disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the performance of the prediction model. A heterogeneous network prediction model (MEAHNE) is proposed to make full use of the complex information contained in multisource data. To fully mine the potential relationship between miRNA and disease, we collected multisource data and constructed a heterogeneous network. After constructing the network, we mined potential associations in the network through a designed heterogeneous network framework (MEAHNE). MEAHNE first learned the semantic information of the metapath instances, then used the attention mechanism to encode the semantic information as attention weights and aggregated nodes of the same type using the attention weights. The semantic information was also integrated into the node. MEAHNE optimized parameters through end-to-end training. MEAHNE was compared with other state-of-the-art heterogeneous graph neural network methods. The values of the area under the precision-recall curve and the receiver operating characteristic curve demonstrated the superiority of MEAHNE. In addition, MEAHNE predicted 20 miRNAs each for breast cancer and nasopharyngeal cancer and verified 18 miRNAs related to breast cancer and 14 miRNAs related to nasopharyngeal cancer by consulting related databases.

Heterogeneous graph neural network for attribute completion

Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN.

An ACO-based cross-layer routing algorithm in space-air-ground integrated networks

Space-air-ground integrated networks (SAGINs) are heterogeneous, self-organizing and time-varying wireless networks providing massive and global connectivity. These three characteristics of SAGINs bring great challenges for routing design. In this paper, the important parameters affecting performance of SAGINs are analyzed, based on which the heterogeneous network framework is described as a vector weighted topology. Instead of a scale, the weighted parameter of the topology is a vector with elements of signal-to-noise ratio (SNR), variation of SNR, end-to-end delay and queuing length. To meet the time-varying requirements, a Wiener predictor is adopted for obtaining the estimated channel information, the expectation of queuing delay is also acquired by modeling the process of packets waiting the transmitting buffer as a M/M/1 queuing system. Considering the Ant Colony Optimization (ACO) algorithm sharing the common decentralized feature with routing algorithm in SAGINs, a novel ACO-based cross-layer routing algorithm for SAGINs is proposed. The proposed algorithm takes the link quality and end-to-end packed delay in the physical layer as deciding factors in searching for optimal routing. Simulations performed in different scenarios show that this proposed algorithm demonstrates a higher packet delivery rate.

MMNET: A Multi-Modal Network Architecture for Underwater Networking

At present, the key to underwater sensor network (UWSN) research is to provide personalized network support for many underwater applications. In order to achieve this goal, people need a general UWSN. Most of the current UWSN architecture is based on the traditional network, which are limited to a single hardware platform and software platform. Facing the current numerous underwater applications and heterogeneous networks, the UWSN is unable to provide personalized network services according to different application requirements. In this paper, we propose a heterogeneous network framework called MMNET (multimodal network) based on the idea of multimodality, aiming to achieve the compatibility of heterogeneous networks and the scalability of the new architecture. In addition, in the face of the complexity of heterogeneous networks and the personalized needs of network applications, the resource allocation is expressed as a personalized recommendation problem. The distributed personalized recommendation algorithm is used to configure personalized network resources for applications. Each node only needs to solve its own problems, instead of exchanging channel state information by using a distributed algorithm, so the computational complexity can be greatly reduced and signaling is overhead. Finally, we give a special example to prove that our network framework provides a good application.

When Network Operation Meets Blockchain: An Artificial-Intelligence-Driven Customization Service for Trusted Virtual Resources of IoT

The Internet of Things (IoT) is evolving from a stovepipe architecture to a collaborative and shared converged architecture. In the future, it will become a network development trend to construct a network-based virtual operating platform by virtualizing the IoT infrastructure to support different IoT services. The trust issues encountered during resource aggregation in heterogeneous networks and state measurements limit the applicability of the virtual network operating platform. In this article, we propose a framework for an endogenous trusted network to support virtual network operation, provide shared services externally, allocate resources internally, and implement artificial-intelligence-driven customization services for trusted virtual resources for the IoT. We use the consortium blockchain to establish the endogenous trusted framework for heterogeneous networks through software-defined networking and network function virtualization. In this framework, we design a mechanism for on-chain and off-chain collaborative resource allocation. The framework integrates time prediction algorithms and resource allocation algorithms to predict resource demand and allocate resources to achieve customization and dynamic adjustment. The application of this framework in specific scenarios is described using application examples.

A Utility-Based Framework for Performance and Energy-Aware Convergence in 5G Heterogeneous Network Environments

The integration of a broadcast oriented RAN architecture into a convergent framework for heterogeneous networks offers two major contributions. On the one hand, it presents an interesting solution to satisfy the exponential growth of the mobile video data traffic, and on the other hand, it is also identified as a promising key technology, that can contribute to improve the energy efficiency of 5G networks. This work proposes a novel Convergent Architecture for Broadcast, Broadband and Cellular services (CABS). This architecture is complemented with the Performance and Energy-aware Access (PEC) network selection algorithm. This joint solution offers a balanced trade-off between the user perceived QoS and the network energy efficiency in challenging heterogeneous scenarios. Furthermore, this approach guarantees the broadcast service continuity in harsh environments, and therefore, is helpful in one of the most critical scenarios for broadcasting industry: mobile and indoor reception. The proposed solution was modelled and tested in Network Simulator version 3 (NS-3), in which among other updates, a first version of an ATSC 3.0 model was introduced.

Deep heterogeneous network embedding based on Siamese Neural Networks

Heterogeneous network embedding aims at mapping a heterogeneous network into a low-dimensional latent space. There exist diverse relations among different types of objects in heterogeneous networks. However, most existing heterogeneous network embedding methods focus on exploring network structures instead of relations among different objects, so some redundant and fuzzy relations are inevitably captured. To address the problem, we propose a Relation-Oriented Deep Embedding (RODE) framework for heterogeneous networks that explores different relations among nodes. The captured relations are modeled through node similarity and dissimilarity. Based on the similarity and dissimilarity, a multi-task Siamese Neural Network is formulated to perform network embedding and optimize embedding representations. Extensive experiments are conducted on four heterogeneous networks. Experimental results demonstrate our method outperforms state-of-the-art embedding algorithms on several network mining tasks, such as link prediction, node classification and node clustering.

Modularity Based Community Detection in Heterogeneous Networks

Heterogeneous networks are networks consisting of different types of nodes and multiple types of edges linking such nodes. While community detection has been extensively developed as a useful technique for analyzing networks that contain only one type of nodes, very few community detection techniques have been developed for heterogeneous networks. In this paper, we propose a modularity based community detection framework for heterogeneous networks. Unlike existing methods, the proposed approach has the flexibility to treat the number of communities as an unknown quantity. We describe a Louvain type maximization method for finding the community structure that maximizes the modularity function. Our simulation results show the advantages of the proposed method over existing methods. Moreover, the proposed modularity function is shown to be consistent under a heterogeneous stochastic blockmodel framework. Analyses of the DBLP four-area dataset and a MovieLens dataset demonstrate the usefulness of the proposed method.

A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus.

West Nile virus (WNV)—a mosquito-borne arbovirus—entered the USA through New York City in 1999 and spread to the contiguous USA within three years while transitioning from epidemic outbreaks to endemic transmission. The virus is transmitted by vector competent mosquitoes and maintained in the avian populations. WNV spatial distribution is mainly determined by the movement of residential and migratory avian populations. We developed an individual-level heterogeneous network framework across the USA with the goal of understanding the long-range spatial distribution of WNV. To this end, we proposed three distance dispersal kernels model: 1) exponential—short-range dispersal, 2) power-law—long-range dispersal in all directions, and 3) power-law biased by flyway direction —long-range dispersal only along established migratory routes. To select the appropriate dispersal kernel we used the human case data and adopted a model selection framework based on approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC). From estimated parameters, we find that the power-law biased by flyway direction kernel is the best kernel to fit WNV human case data, supporting the hypothesis of long-range WNV transmission is mainly along the migratory bird flyways. Through extensive simulation from 2014 to 2016, we proposed and tested hypothetical mitigation strategies and found that mosquito population reduction in the infected states and neighboring states is potentially cost-effective.

Video Transcoding, Caching, and Multicast for Heterogeneous Networks Over Wireless Network Virtualization

To support the massive content delivery, a novel virtualized heterogeneous network framework is proposed to enable content transcoding, caching, and multicast. In addition, a virtual multi-resources allocation problem is formulated to jointly optimize the utilities of computing, caching, and communication for the proposed framework. Simulations are conducted to show the effectiveness of the proposed scheme.

A Cell Outage Management Framework for Dense Heterogeneous Networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes—a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., $k$ -nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner.

PicoGrid: A Web-Based Distributed Computing Framework for Heterogeneous Networks Using Java

We propose a framework for distributed computing applications in heterogeneous networks. The system is simple to deploy and can run on any operating systems that support the Java Virtual Machine. Using our developed system, idle computing power in an organization can be harvested for performing computing tasks. Agent computers can enter and leave the computation at any time which makes our system very flexible and easily scalable. Our system also does not affect the normal use of client machines to guarantee satisfactory user experience. System tests show that the system has comparable performance to the theoretical case and the computation time is significantly reduced by utilizing multiple computers on the network.

An energy efficient and spectrum efficient wireless heterogeneous network framework for 5G systems

In this article we explore a system framework of cooperative green heterogeneous networks for 5G wireless communication systems. We first survey the state-of-the-art on spectrum efficiency (SE), energy efficiency (EE), and quality of service (QoS) based mobile association, multi-layer interference management and power control, network wide cooperation and dynamic resource allocation for heterogeneous wireless networks. We also present the system framework of cooperative green heterogeneous networks, which aims at balancing and optimizing SE, EE, and QoS in heterogeneous wireless networks. We discuss the design principles and show some preliminary performance results on the tradeoffs among SE, EE, and QoS. Finally, we identify the technical challenges that remain in the cooperative green heterogeneous network design. The presented wireless system framework is expected to advance the understandings of the critical technical issues toward energy and spectrum efficient 5G wireless communication systems.

Resource Allocation Schemes for the Heterogeneous OFDMA System with Multiple Ad Hoc Relays

In this paper, we present a heterogeneous network framework involved an orthogonal frequency division multiple access (OFDMA) network with an out-of-band Ad Hoc network, which maybe a promising architectural upgrade and has not been studied so far. In the heterogeneous system, the mobile stations (MSs) are dual-mode with both cellular OFDMA and Ad Hoc radios. And MSs communicate with the base station by cellular OFDMA mode, while they communicate with each other by Ad Hoc mode. An active MS can choose multiple inactive MSs as its relay stations (RSs). And each RS operates in a full duplex mode, that is, it can receive data through Ad Hoc network in the first hop and transmit data through the OFDMA network in the second hop, simultaneously. Based on this heterogeneous system, the problem of joint relay selection, subcarrier and power allocation with the objective of maximizing the system capacity under individual rate and transmit power constraints is addressed. We propose a dual-based resource allocation scheme to solve the problem fairly and efficiently, and then we propose a fully distributed resource allocation scheme to decrease the computational complexity. Simulation results show that the performance of the proposed dual-based resource allocation scheme, in terms of the system capacity and the outage probability, is better than that of the fully distributed resource allocation scheme. However, the performance improvement is at the expense of a high computational complexity.

Topic level expertise search over heterogeneous networks

In this paper, we present a topic level expertise search framework for heterogeneous networks. Different from the traditional Web search engines that perform retrieval and ranking at document level (or at object level), we investigate the problem of expertise search at topic level over heterogeneous networks. In particular, we study this problem in an academic search and mining system, which extracts and integrates the academic data from the distributed Web. We present a unified topic model to simultaneously model topical aspects of different objects in the academic network. Based on the learned topic models, we investigate the expertise search problem from three dimensions: ranking, citation tracing analysis, and topical graph search. Specifically, we propose a topic level random walk method for ranking the different objects. In citation tracing analysis, we aim to uncover how a piece of work influences its follow-up work. Finally, we have developed a topical graph search function, based on the topic modeling and citation tracing analysis. Experimental results show that various expertise search and mining tasks can indeed benefit from the proposed topic level analysis approach.

Scheduling security-critical multimedia applications in heterogeneous networks

Security is becoming an increasingly important issue in the design of multimedia applications, which are widely used in the industry and academic organizations. However, existing scheduling schemes for real-time multimedia service in heterogeneous networks generally do not take into account security requirements when making allocation and control decisions. In this paper, we develop and evaluate a security-critical multimedia scheduling scheme in the framework of heterogeneous networks. At first, we construct a general media distortion model according to the observed parameters in each network, as well as each application’s characteristic. After that, we exploit a scalable graph-based authentication method which achieves a good trade-off between flexibility and efficiency. Furthermore, a security-critical scheduling scheme is proposed by taking into account applications’ timing and security requirements in addition to precedence constraints. The proposed scheme is applied to heuristically find resource allocations, which maximize the quality of security and the probability of meeting deadlines for all the multimedia applications running on heterogeneous networks. Extensive simulations are provided to demonstrate the effectiveness and feasibility of the proposed scheme.

SIP-based proactive and adaptive mobility management framework for heterogeneous networks

In this paper, we present and evaluate the performance of a mobility management system called the Proactive and Adaptive Handover (PAHO) system. PAHO is an application-level approach that uses SIP to manage client-initiated connection handoff across heterogeneous networks based on the IEEE 802.21 framework with designated user/configuration policy. Unlike conventional systems which make sub-optimal decision when managing connection handoff due to limited awareness of the relevant context for the application/service being delivered, PAHO defines proper interface to interact with the application as to determine when and to where the handoff and/or codec switching should take place in the event of network performance degradation. The results showed that using the PAHO approach on an audio/video conferencing session helps reducing the overall handover delay from 10.766 s (on non-PAHO system) down to at least 288 ms, and slowing down the degradation of MOS value throughout the entire experiment in the event of signal degradation as well as network congestion. It is also shown that load balancing among the access points (AP) could be achieved with an improved Information Server (IS).

A framework of handoffs in wireless overlay networks based on mobile IPv6

Although there are various wireless access network technologies with different characteristics and performance level have been developed, no single network that can satisfy the anytime, anywhere, and any service wireless access needs of mobile users. A truly seamless mobile environment can only be realized by considering vertical and horizontal handoffs together. With the advantages of Mobile IPv6, a more comprehensive and integrated framework of heterogeneous networks can be developed. In this paper, we discuss the issues related to handoffs including horizontal and vertical handoffs. We present a scheme for integrating wireless local area network and wide area access networks, and propose a micromobility management method called HiMIPv6+. We also propose a QoS-based (quality-of-service-based) vertical handoff scheme and algorithm that consider wireless network transport capacity and user service requirement. Our prototype evaluations and the simulations show that our framework performs as expected.