Heterogeneous Network Model Research Articles

A General Recommendation Model for Heterogeneous Networks

Heterogeneous networks refer to the networks comprising multiple types of entities as well as their interaction relationships. They arise in a great variety of domains, for example, event-based social networks Meetup and Plancast, and DBLP. Recommendation is a useful task in these heterogeneous network systems. Although many recommendation algorithms are proposed for heterogeneous data, none of them is able to explicitly model the influence strength between different types of entities, which is useful not only for achieving higher recommendation accuracy but also better understanding the role of each entity type in recommendation problems. Moreover, many of those algorithms are designed for a particular task, and hence it is challenging to apply them in other problems. In this paper, we propose a graph-based model, called HeteRS, which can solve general recommendation problems on heterogeneous networks. Our method models the rich information with a heterogeneous graph and considers the recommendation problem as a query-dependent node proximity problem. To address the challenging issue of weighting the influences between different types of entities, we propose a learning scheme to set the influence weights between different types of entities in recommendation. Experimental results on real-world datasets demonstrate that our proposed method significantly outperforms the baseline methods in our experiments for all the recommendation tasks, and the learned influence weights help understanding user behaviors.

Energy efficient multilevel network model for heterogeneous WSNs

In this paper, we propose a multilevel heterogeneous network model that is characterized by two types of parameters: primary and secondary parameters. The primary parameter decides the level of heterogeneity and the secondary parameters are decided according to the level of heterogeneity. This model can describe a network in which the nodes can have up to nth level of energy (n is a finite number) depending upon the parameter values. We evaluate the performance of the HEED, a clustering protocol, using this model and name the resultant protocol as MLHEED (Multi Level HEED) protocol. For n level of heterogeneity, this protocol is denoted by MLHEED-n. The numbers of nodes of each type in any level of heterogeneity are determined by the secondary model parameter. The MLHEED protocol (for all level heterogeneity) considers two parameters for deciding the cluster heads, i.e., residual energy and node density. In this work, we illustrate the network model up to six levels. Experimentally, as the level of heterogeneity increases, the rate of energy dissipation decreases and hence the nodes stay alive for longer time. The MLHEED-1, MLHEED-2, MLHEED-3, MLHEED-4, MLHEED-5, and MLHEED-6 increase the network lifetime by 73.05%, 143.40%, 213.17%, 267.90%, 348.60%, respectively, by increasing the network energy as 40%, 57%, 68.5%, 78%, 84%, with respect to the original HEED protocol.

Energy efficient heterogeneous DEEC protocol for enhancing lifetime in WSNs

In this paper, we propose a 3-level heterogeneous network model for WSNs to enhance the network lifetime, which is characterized by a single parameter. Depending upon the value of the model parameter, it can describe 1-level, 2-level, and 3-level heterogeneity. Our heterogeneous network model also helps to select cluster heads and their respective cluster members by using weighted election probability and threshold function. We compute the network lifetime by implementing DEEC protocol for our network model. The DEEC implementation for the existing 1-level, 2-level, and 3-level heterogeneous network models are denoted as DEEC-1, DEEC-2, and DEEC-3, respectively, and for our proposed 3-level heterogeneous network model, the DEEC implementations are denoted as hetDEEC-1, hetDEEC-2, and hetDEEC-3, respectively. The network lifetime in DEEC-3 and hetDEEC-3 increases by 154.17% and 182.67%, respectively by increasing the total network energy 100% with respect to the original DEEC.

The Performance of Artificial Neural Networks and Tier-Structured Information Transmission in Identifying Aggregate Consumption Patterns in New Zealand

This study explores the value of information transmission in training heterogeneous Artificial Neural Network (ANN) models to identify patterns in the growth rate of aggregate per-capita consumption spending in New Zealand. A tier structure is used to model how information passes from one ANN to another. A group of ‘tier 1’ ANNs are first trained to identify consumption patterns using economic data. ANNs in subsequent tiers are also trained to identify consumption patterns, but they use the patterns constructed by ANNs trained in the preceding tier (secondary information) as inputs. The model's results suggest that it is possible for ANNs downstream to outperform ANNs trained using empirical data directly on average. This result, however, varies from time period to time period. Increasing access to secondary information is shown to increase the similarity of heterogeneous predictions by ANNs in lower tiers, but not substantially affect average accuracy.

Design, Modeling, and Performance Analysis of Multi-Antenna Heterogeneous Cellular Networks

This paper presents a stochastic geometry-based framework for the design and analysis of downlink multi-user multiple-input multiple-output (MIMO) heterogeneous cellular networks with linear zero-forcing transmit precoding and receive combining, assuming Rayleigh fading channels and perfect channel state information. The generalized tiers of base stations may differ in terms of their Poisson point process spatial density, number of transmit antennas, transmit power, artificial-biasing weight, and number of user equipments served per resource block. The spectral efficiency of a typical user equipped with multiple receive antennas is characterized using a non-direct moment-generating-function-based methodology with closed-form expressions of the useful received signal and aggregate network interference statistics systematically derived. In addition, the area spectral efficiency is formulated under different space-division multiple-access and single-user beamforming transmission schemes. We examine the impact of different cellular network deployments, propagation conditions, antenna configurations, and MIMO setups on the achievable performance through theoretical and simulation studies. Based on the state-of-the-art system parameters, the results highlight the inherent limitations of baseline single-input single-output transmission and conventional sparse macro-cell deployment, as well as the promising potential of multi-antenna communications and small-cell solution in interference-limited cellular environments.

Stochastic Geometry Modeling and System-Level Analysis of Uplink Heterogeneous Cellular Networks With Multi-Antenna Base Stations

In this paper, mathematical frameworks for system-level analysis and design of uplink heterogeneous cellular networks with multiple antennas at the base station (BS) are introduced. Maximum ratio combining (MRC) and optimum combining (OC) at the BSs are studied and compared. A generalized cell association criterion and fractional power control scheme are considered. The locations of all tiers of BSs are modeled as points of homogeneous and independent Poisson point processes. With the aid of stochastic geometry, coverage probability and average rate are formulated in integral but mathematically and computationally tractable expressions. Based on them, performance trends for small- and large-scale multiple-antenna BSs are discussed. Coverage and rate are shown to highly depend on several parameters, including the path-loss exponent, the fractional power control compensation factor, and the maximum transmit power of the mobile terminals. The gain of OC compared with MRC is proved to increase, if a more aggressive power control is used and if the number of BS antennas increases but is finite. For the same number of BS antennas, OC is shown to reach the noise-limited asymptote faster than MRC. All findings are validated via Monte Carlo simulations.

Multilevel heterogeneous network model for wireless sensor networks

The lifetime of a network can be increased by increasing the network energy. The network energy can be increased either increasing the number of sensors or increasing the initial energy of some sensors without increasing their numbers. Increasing network energy by deploying extra sensors is about ten times costlier than that using some sensors of high energy. Increasing the initial energy of some sensors leads to heterogeneous nodes in the network. In this paper, we propose a multilevel heterogeneous network model that is characterized by two types of parameters: primary parameter and secondary parameters. The primary parameter decides the level of heterogeneity by assuming the values of secondary parameters. This model can describe a network up to nth level of heterogeneity (n is a finite number). We evaluate the network performance by applying the HEED, a clustering protocol, on this model, naming it as MLHEED (Multi Level HEED) protocol. For n level of heterogeneity, this protocol is denoted by MLHEED-n. The numbers of nodes of each type in any level of heterogeneity are determined by the secondary model parameter. The MLHEED protocol (for all level heterogeneity) considers two variables, i.e., residual energy and node density, for deciding the cluster heads. We also consider fuzzy implementation of the MLHEED in which four variables are used to decide the cluster heads: residual energy, node density, average energy, and distance between base station and the sensor nodes. In this work, we illustrate the network model up to seven levels ($$1\le n\le 7$$1≤n≤7). Experimentally, as the level of heterogeneity increases, the rate of energy dissipation decreases and hence the nodes stay alive for longer time. The MLHEED-m, $$m=2,3,4,5,6,7$$m=2,3,4,5,6,7, increase the network lifetime by $$73.05, 143.40, 213.17, 267.90, 348.60, 419.10\,\%$$73.05,143.40,213.17,267.90,348.60,419.10%, respectively, by increasing the network energy as $$40, 57, 68.5, 78, 84, 92.5\,\%$$40,57,68.5,78,84,92.5% with respect to the original HEED protocol. In case of fuzzy implementation, the MLHEEDFL-m, $$m=2,3,4,5,6,7,$$m=2,3,4,5,6,7, increases the network lifetime by $$282.7, 378.5, 435.78, 498.50, 582.63, 629.79\,\%$$282.7,378.5,435.78,498.50,582.63,629.79%, respectively, corresponding to the same increase in the network energy as that of the MLHEED (all levels) with respect to the original HEED. The fuzzy implementation of the HEED, MLHEEDFL-1, increases the network lifetime by $$176.6\,\%$$176.6% with respect to the original HEED with no increase in the network energy.

Global energy investment structure from the energy stock market perspective based on a Heterogeneous Complex Network Model

Energy is not only an important international commodity but is also an important financial product. Many studies address the global energy structure by considering energy as commodity, but few study it based on energy’s financial attributes. In this paper, we analyzed the global energy investment structure based on the shareholding relationships between listed energy companies and their shareholders by taking the frontier approach to complex network theory and treating these relationships as a heterogeneous complex network from a global angle. We constructed a primitive investment network of listed energy companies and their shareholders (two sets of actors). Then, based on the two-mode affiliation relationships between two sets of actors and countries, we constructed a derivative investment network of 112 countries (and regions). By calculating different topological features, we quantitatively analyzed the national diversity of outward and inward energy investment, the energy investment preference, the strength of the bilateral energy investment relationship between countries (and regions) and the most powerful group of energy investing countries based on the shareholding relationships of global listed energy companies. It is found that the vast majority of outward and inward investment relationships are still in the hands of a few countries: traditional developed countries, emerging countries, and a few particular island countries (and regions) that are tax havens. This study is helpful for analyzing the roles and investment relationships of nations as well as the global energy investment structure taking the perspective of the energy stock market.

Capacity of Heterogeneous Mobile Wireless Networks with D-Delay Transmission Strategy.

This paper investigates the capacity problem of heterogeneous wireless networks in mobility scenarios. A heterogeneous network model which consists of n normal nodes and m helping nodes is proposed. Moreover, we propose a D-delay transmission strategy to ensure that every packet can be delivered to its destination nodes with limited delay. Different from most existing network schemes, our network model has a novel two-tier architecture. The existence of helping nodes greatly improves the network capacity. Four types of mobile networks are studied in this paper: i.i.d. fast mobility model and slow mobility model in two-dimensional space, i.i.d. fast mobility model and slow mobility model in three-dimensional space. Using the virtual channel model, we present an intuitive analysis of the capacity of two-dimensional mobile networks and three-dimensional mobile networks, respectively. Given a delay constraint D, we derive the asymptotic expressions for the capacity of the four types of mobile networks. Furthermore, the impact of D and m to the capacity of the whole network is analyzed. Our findings provide great guidance for the future design of the next generation of networks.

Small Cell Base Station Sleep Strategies for Energy Efficiency

Small cell networks offer a promising and viable approach to meeting the increasing demand for high-data-rate wireless applications. With the expected increase in the number of small cell deployments, energy efficiency (EE) is a crucial system design parameter that demands consideration from an eco-sustainability perspective. One way to improve EE is to switch off small cell base stations (BSs) or to keep them in energy-saving mode while preserving the quality of service (QoS) experienced by users. With “bits/joule” as the metric, we aim to optimize EE with the introduction of several levels of sleep depths. Using a stochastic geometry-based heterogeneous cellular network (HCN) model, we derive coverage probability, average achievable rate, and EE in heterogeneous $K$ -tier wireless networks with different sleep modes for small cells. Then, we try to maximize EE under 1) a random sleeping policy and 2) a strategic sleeping policy, with constraints on both coverage probability and wake-up times. Due to the nonconvexity of EE, we propose an alternative low-complexity near-optimal solution by maximizing the lower bound of EE. We use an alternating iterative approach to solve the resulting multivariable optimization problem. Simulation results confirm the effectiveness of the scheme. With improvements of approximately 30% in EE with random sleeping policy, simulation indicates that instantaneous EE can be further improved by 15% with a strategic sleeping policy.

Visible light communications heterogeneous network (VLC-HetNet): new model and protocols for mobile scenario

In order to resolve the crowded radio spectrum of wireless communication systems, visible light communications (VLC), which uses a vast unregulated and free light spectrum, has emerged to be a viable solution. However, duplex communication, user mobility, multi-user access and transmission mechanisms are becoming challenging tasks in VLC network. In this article, we propose a new VLC heterogeneous network (VLC-HetNet) model which merges VLC and radio frequency (RF) network. VLC channel is only used for downlink transmission, while RF channels are served for uplinks in any situation, or for downlinks only without VLC hotspots coverage. New VLC frame, multi-user access mechanism, horizontal and vertical handover protocols are presented to support the VLC-HetNet model, especially for solving the problems at multi-user mobility scenario. Simulation results show improvements in capacity performance of the VLC-HetNet, when compared to RF system. Besides, the average vertical handover number and VLC downlink dwelling time ratio have been analysed.

A novel probabilistic clustering model for heterogeneous networks

Heterogeneous networks, consisting of multi-type objects coupled with various relations, are ubiquitous in the real world. Most previous work on clustering heterogeneous networks either converts them into homogeneous networks or simplifies the modeling of the heterogeneity in terms of specific objects, structures or assumptions. However, few studies consider all relevant objects and relations, and trade-off between integrating relevant objects and reducing the noises caused by relations across objects. In this paper, we propose a general probabilistic graphical model for clustering heterogeneous networks. First, we present a novel graphical representation based on our basic assumptions: different relation types produce different weight distributions to specify intra-cluster probability between two objects, and clusters are formed around cluster cores. Then, we derive an efficient algorithm called PROCESS, standing for PRObabilistic Clustering modEl for heterogeneouS networkS. PROCESS employs a balance-controlled message passing algorithm and mathematical programming for inference and estimation. Experimental results show that our approach is effective and significantly outperforms the state-of-the-art algorithms on both synthetic and real data from heterogeneous networks.

A Circular Interference Model for Heterogeneous Cellular Networks

Scaling up the number of base stations per unit area and augmenting the heterogeneity of networks are two major trends in mobile cellular systems of the fourth (4G)- and fifth generation (5G), making it increasingly difficult to characterize aggregate interference statistics with system models that are characterized by a low number of parameters. This paper proposes a new circular interference model that aggregates given interferer topologies to power profiles along circles. We introduce a mapping procedure that preserves the aggregate interference statistics at arbitrary user locations within the associated cell with a certain desired accuracy. At the same time, our method identifies the amount of nodes in a given interferer topology that principally determine the shape of the interference distribution. To further refine the analysis of given power profiles, a new finite sum representation for the sum of Gamma random variables with integer-valued shape parameter is introduced. The approach allows decomposing the distribution of the aggregate interference into the contributions of the individual interferers. Such knowledge is particularly expedient for the application of base station coordination- and cooperation schemes. Moreover, the proposed method enables accurate prediction of the corresponding signal-to-interference-ratio- and rate statistics.

Collaborative Production Structure of Knowledge Sharing Behavior in Internet Communities

Peer production has received considerable attention because it is a new mode of providing information. Online encyclopedias are one typical application of peer production. They allow anyone to freely create and share knowledge. Large numbers of volunteers participate and form various relationships in the process of creating and sharing knowledge. A heterogeneous network model is presented to analyze the collaborative relationship of sharing behavior, and a new network measurement is presented to investigate the network structure by mapping it into a quantum system. The results of the present work show that volunteers in online communities are heterogeneous and that the collective states induced by these collaborative production network structures are different at different times. Some collective states are ordered states while others are softly chaotic. Analysis of these collective states may provide completely different insight from the individual dynamic state of contributors in a network.

Energy-efficient mobile cluster-head data collection model for wireless sensor networks

Wireless sensor networks have grown from homogeneous network architecture to heterogeneous networks for the improvement of network efficiency and lifetime. This paper exhibits a mobile cluster-head data collection (MCHDC) model for heterogeneous wireless networks. The MCHDC model illustrates the mobility pattern of mobile cluster-head nodes in a controlled environment with uniform and nonuniform deployment of mobile elements in the area of observation. It focuses on the threshold velocity for movement of mobile elements in the network area. In this paper the effects of data sending rate, network area, sensor node density, and cluster-head node density on energy efficiency of the network with the MCHDC model have been studied through simulations.

A distributed semantic filtering model based on approximate automata for heterogeneous multi-sensor networks

An increasing number of sensor networks are deployed to collect information on a wide range of applications. However, these sensor networks are isolated to each other. Meanwhile, processing large-scale of data stream is the neck of the push-mode system. To match the huge event stream in heterogeneous sensor network data, we propose approximation semantic factor ASR, according to which we further propose the approximately semantic matching model for heterogeneous multi-sensor networks called approximately semantic matching model for heterogeneous multi-sensor networks ASMMN. Furthermore, the evaluate pattern algorithm is put forward. ASMMN can effectively solve the semantic coupling and speed matching. Simulation results show that the proposed model can accelerate the matching process and reduce memory consumption of intermediate states among event streams. Meanwhile, the model has a good scalability that can be widely used in the heterogeneous push-mode sensor networks and internet of things IOTs.

An Efficient Scheduling Method Based on Pulse-coupled Oscillator Model for Heterogeneous Large-scale Wireless Sensor Networks

Wireless sensor networks (WSNs) have been common networking technologies for data gathering applications. In order to collect necessary data effectively, such applications require large-scale WSNs many sensor nodes are deployed widely. As its solution, IEEE 802.11ah is promising. However, it operates at sub 1GHz band that is license-free, which may result in that different service providers deploy WSNs for different purposes. This incurs serious collisions due to hidden nodes. Unfortunately, they often refuse cooperation among the others due to their service policies. Therefore, self-organized scheduling methods are needed without proactive cooperation. To this end, in this paper, we propose a self-organized scheduling method for large-scale WSNs, which is based on the pulse-coupled oscillator model. To avoid collisions effectively, the proposed method utilizes a phase response function that has attractors corresponding to time slots and a random mechanism for slot selection. Through simulation-based evaluation, we demonstrate that the proposed method can collect about 90% of data in a situation sensor nodes have different cycles of data gathering while achieving a reasonable convergence time. We also show its good flexibility for environmental changes.

Inferring Disease Associated Phosphorylation Sites via Random Walk on Multi-Layer Heterogeneous Network

As protein phosphorylation plays an important role in numerous cellular processes, many studies have been undertaken to analyze phosphorylation-related activities for drug design and disease treatment. However, although progresses have been made in illustrating the relationship between phosphorylation and diseases, no existing method focuses on disease-associated phosphorylation sites prediction. In this work, we proposed a multi-layer heterogeneous network model that makes use of the kinase information to infer disease-phosphorylation site relationship and implemented random walk on the heterogeneous network. Experimental results reveal that multi-layer heterogeneous network model with kinase layer is superior in inferring disease-phosphorylation site relationship when comparing with existing random walk model and common used classification methods.

Minimized-cost cube query on heterogeneous information networks

Data cube is the foundation of on-line analytical processing (OLAP), which can provide users with data views from different perspectives and granularities. Heterogeneous information networks consist of multiple types of nodes and edges which represent different semantic relations. With the rapid development of social networks and knowledge graphs, heterogeneous information networks have become increasingly popular. In heterogeneous information networks, cube is the set of aggregate graphs and cube query is required for supporting OLAP. The existing research mainly studies aggregate graph query on homogeneous networks, but only considers the attributes of nodes. To overcome these challenges, this paper investigates cube query problem on heterogeneous information networks. (1) A novel cube model for heterogeneous information networks is proposed, which captures both the attribute and structure semantics. (2) Because the total number of aggregate graphs is huge, computing and storing them cost plenty of time and storage. The problem of partial cube materialization on heterogeneous information networks is investigated. Given a fixed size of memory space, select a subset of aggregate graphs in cube, to minimize the computing cost of the whole cube. This optimization problem is proved to be NP-complete and there is no $$n^{1-{\epsilon }}$$n1-∈ approximation algorithm unless P $$=$$= NP. (3) A greedy algorithm is proposed for partial cube materialization based on two interesting dependencies between aggregate graphs, attribute dependence and path dependence. (4) Experiments on real world data sets show the cube definition is meaningful, and the partial cube materialization algorithm is efficient.

Patterns of recruitment and injury in a heterogeneous airway network model.

In respiratory distress, lung airways become flooded with liquid and may collapse due to surface-tension forces acting on air–liquid interfaces, inhibiting gas exchange. This paper proposes a mathematical multiscale model for the mechanical ventilation of a network of occluded airways, where air is forced into the network at a fixed tidal volume, allowing investigation of optimal recruitment strategies. The temporal response is derived from mechanistic models of individual airway reopening, incorporating feedback on the airway pressure due to recruitment. The model accounts for stochastic variability in airway diameter and stiffness across and between generations. For weak heterogeneity, the network is completely ventilated via one or more avalanches of recruitment (with airways recruited in quick succession), each characterized by a transient decrease in the airway pressure; avalanches become more erratic for airways that are initially more flooded. However, the time taken for complete ventilation of the network increases significantly as the network becomes more heterogeneous, leading to increased stresses on airway walls. The model predicts that the most peripheral airways are most at risk of ventilation-induced damage. A positive-end-expiratory pressure reduces the total recruitment time but at the cost of larger stresses exerted on airway walls.