Different Classes Of Nodes Research Articles

Identifying multiple influential spreaders in complex networks based on spectral graph theory

One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network. The importance and dispersion of critical nodes among them are both vital factors that can influence maximization. We therefore propose a multiple influential spreaders identification algorithm based on spectral graph theory. This algorithm first quantifies the role played by the local structure of nodes in the propagation process, then classifies the nodes based on the eigenvectors of the Laplace matrix, and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each other. Experimental results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.

Bipartite network embedding with Symmetric Neighborhood Convolution

Mining bipartite networks, a ubiquitous type of model particularly developed for networks with two different classes of nodes, is of great importance in various applications. Recently, the advance of network representation learning has provided more tractable, effective and robust methods for several graph mining tasks. However, the strength of representation learning has not been sufficiently explored for bipartite structure, since that most existing learning methods either separately treat the nodes in different classes by defective projections, or independently sample instances for learning by adopting relation-aware random walks. Both methods can lose information and they hardly leverage the symmetry property of bipartite structure directly. In this paper, a novel method called Symmetric Neighborhood Convolution is proposed to fulfill the representation learning task in bipartite networks. The method takes into consideration the topological symmetry property and utilizes one-dimensional convolution kernels to extract features from neighborhood for the nodes in each class. Three different objectives are designated to compare the performance of the proposed convolution-based mechanism. The method is evaluated on five real-world datasets by the tasks of link prediction and recommendation. Comparing to eight competitive baselines, experimental results demonstrate the effectiveness of our method.

A Random Walk Approach to Transport in Tissues and Complex Media: From Microscale Descriptions to Macroscale Models.

The biological processes necessary for the development and continued survival of any organism are often strongly influenced by the transport properties of various biologically active species. The transport phenomena involved vary over multiple temporal and spatial scales, from organism-level behaviors such as the search for food, to systemic processes such as the transport of oxygen from the lungs to distant organs, down to microscopic phenomena such as the stochastic movement of proteins in a cell. Each of these processes is influenced by many interrelated factors. Identifying which factors are the most important, and how they interact to determine the overall result is a problem of great importance and interest. Experimental observations are often fit to relatively simple models, but in reality the observations are the output of complicated functions of the physicochemical, topological, and geometrical properties of a given system. Herein we use multistate continuous-time random walks and generalized master equations to model transport processes involving spatial jumps, immobilization at defined sites, and stochastic internal state changes. The underlying spatial models, which are framed as graphs, may have different classes of nodes, and walkers may have internal states that are governed by a Markov process. A general form of the solutions, using Fourier-Laplace transforms and asymptotic analysis, is developed for several spatially infinite regular lattices in one and two spatial dimensions, and the theory is developed for the analysis of transport and internal state changes on general graphs. The goal in each case is to shed light on how experimentally observable macroscale transport coefficients can be explained in terms of microscale properties of the underlying processes. This work is motivated by problems arising in transport in biological tissues, but the results are applicable to a broad class of problems that arise in other applications.

Modeling of Duty-Cycled MAC Protocols for Heterogeneous WSN with Priorities

Wireless Sensor Networks (WSN) have experienced an important revitalization, particularly with the arrival of Internet of Things applications. In a general sense, a WSN can be composed of different classes of nodes, having different characteristics or requirements (heterogeneity). Duty-cycling is a popular technique used in WSN, that allows nodes to sleep and wake up periodically in order to save energy. We believe that the modeling and performance evaluation of heterogeneous WSN with priorities operating in duty-cycling, being of capital importance for their correct design and successful deployment, have not been sufficiently explored. The present work presents a performance evaluation study of a WSN with these features. For a scenario with two classes of nodes composing the network, each with a different channel access priority, an approximate analytical model is developed with a pair of two-dimensional discrete-time Markov chains. Note that the same modeling approach can be used to analyze networks with a larger number of classes. Performance parameters such as average packet delay, throughput and average energy consumption are obtained. Analytical results are validated by simulation, showing accurate results. Furthermore, a new procedure to determine the energy consumption of nodes is proposed that significantly improves the accuracy of previous proposals. We provide quantitative evidence showing that the energy consumption accuracy improvement can be up to two orders of magnitude.

CTRA: A complex terrain region-avoidance charging algorithm in Smart World

The emergence of “Smart World” is set to enhance daily necessities with abilities of sensing, communication, computation and intelligence so that many tasks and processes could be simplified, and made more efficient and enjoyable. The realization of Smart World requires information sensing as the foundation of future applications, thus wireless sensor networks (WSNs) are highly called for deployment. However, the limited energy of nodes has always restricted the application of WSNs. Wireless rechargeable sensor networks (WRSNs) introduce mobile chargers in WSNs to provide energy to low-powered sensor nodes through wireless charging technologies, thus solving energy problems effectively. Many studies have designed charging algorithms in normal environment without considering terrain complexity. The complex terrain causes difficulties in the movement and the path planning of the charger. In this paper, we propose a complex terrain region-avoidance charging algorithm (CTRA) in WRSNs. The algorithm aims to optimize the node energy replenishment, minimize the energy consumption of nodes in complex terrain and reduce the frequency the charger visits the complex terrain. The CTRA comprises three parts: the network is divided into small charging regions based on terrain complexity; nodes are classified into three classes based on the terrain where they are located, and different data routing protocols are designed for different classes of nodes in order to reduce the energy consumption on nodes in complex terrain areas; three charging schemes are designed in different terrain areas for the mobile charger. Simulation experiments indicate that the charging efficiency of the CTRA is greatly improved, and the number of dead nodes are also reduced effectively.

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug–target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.

Natural synchronization in power-grids with anti-correlated units

Synchronization between spatially distributed nodes of a power-grid is a crucial requirement for its proper operation. Using a Kuramoto-like network as a realistic physical model for the distribution of electrical power in a power-grid, we obtain coupling strengths and topological characteristics that favor the synchronous state of those technological networks. Power-grids are highly heterogeneous. They are composed of different classes of nodes and each node behaves differently. We show in this work that if a power-grid is extensive and nodes are highly connected, the coupling strength that leads to synchronization depends mainly on the eigenvalues of the Laplacian matrix, as it happens in homogeneous networks composed of equal nodes. On the other hand, if a power-grid is sparsely connected, the coupling strength that leads to synchronization is also strongly related to the correlation coefficient of the network, which means that a high number of connections between similar nodes (two power plants or two consumer centers) disfavor the synchronizability of the power-grid. We apply our results to the Brazilian power-grid system.

Throughput analysis of one-dimensional vehicular ad hoc networks

We consider content distribution in a one-dimensional vehicular ad hoc network. We assume that a file is encoded using fountain code, and the encoded message is cached at infostations. Vehicles are allowed to download data packets from infostations, which are placed along a highway. In addition, two vehicles can exchange packets with each other when they are in proximity. As long as a vehicle has received enough packets from infostations or from other vehicles, the original file can be recovered. In this work, we derive closed-form expressions for the average per-node throughput, under both discrete and continuous velocity distributions for the vehicles. Our result shows that the average per-node throughput can be expressed as a linear function of the densities of different classes of nodes in the highway. This result implies that the average per-node throughput scales linearly with vehicle arrival rate, and the average system throughput scales quadratically with vehicle arrival rate. Besides, system throughput reduces when overall mobility increases.

Complexity in human transportation networks: a comparative analysis of worldwide air transportation and global cargo-ship movements

We present a comparative network-theoretic analysis of the two largest global transportation networks: the worldwide air-transportation network (WAN) and the global cargo-ship network (GCSN). We show that both networks exhibit surprising statistical similarities despite significant differences in topology and connectivity. Both networks exhibit a discontinuity in node and link betweenness distributions which implies that these networks naturally segregate into two different classes of nodes and links. We introduce a technique based on effective distances, shortest paths and shortest path trees for strongly weighted symmetric networks and show that in a shortest path tree representation the most significant features of both networks can be readily seen. We show that effective shortest path distance, unlike conventional geographic distance measures, strongly correlates with node centrality measures. Using the new technique we show that network resilience can be investigated more precisely than with contemporary techniques that are based on percolation theory. We extract a functional relationship between node characteristics and resilience to network disruption. Finally we discuss the results, their implications and conclude that dynamic processes that evolve on both networks are expected to share universal dynamic characteristics.

Orthogonal convolutional modulation for UWB-impulse radio systems: performance analysis and adaptive schemes

In the context of UWB (ultra-wide band) impulse radio (IR) heterogeneous low data-rate networks, the need for coding/modulation schemes amenable of both coherent and noncoherent detection has emerged. The rationale is the potential coexistence of different classes of nodes, characterized by different complexity and performances. In such scenario, one of the main technical challenges is the design of efficient coding/modulation schemes incorporating forward error correction (FEC) and spreading capabilities, thus providing the necessary processing and coding gain, to guarantee robust communications. In this work, we introduce novel coding schemes exploiting the trellis structure of orthogonal convolutional codes for generating time-hopping sequences suitable for UWB-IR, while simultaneously mapping the information bits onto the transmitted waveforms. Namely, we present a new class of orthogonal convolutional modulation (OCM) schemes, highlighting the trade-offs in terms of coherent vs. non-coherent detection performance. Then we develop an adaptive OCM scheme (AOCM) for application to broadcast transmission in heterogeneous UWB-IR networks, illustrating advantages in terms of energy savings and potential drawbacks.

When are networks truly modular?

The study of cluster or community structure of complex networks contributes to the understanding of networks at a functional level. In many networks, latent classes of nodes are suspected which manifest themselves as communities, i.e. groups of nodes with a high link density among the nodes of the same class and low link density between nodes of different classes. Community detection algorithms are used to infer these classes, e.g. by finding a partition of the network which maximizes a quality function such as the network modularity Q [M. Newman, M. Girvan, Finding and evaluating community structure in networks, Phys. Rev. E 69 (2004) 026113]. However, it is known from numerical experiments that even purely random networks display intrinsic modularity and may be partitioned yielding high values of Q . Extending on our earlier results [J. Reichardt, S. Bornholdt, Statistical mechanics of community detection, Phys. Rev. E 74 (2006) 016110], the mapping of the community detection problem onto finding the ground state of a spin glass is exploited in order to derive analytical expressions for the expected modularity in random graphs and assess the theoretical limits to community detection. The results are independent of any specific community detection algorithm and allow for differentiation between modularity arising purely due to the search process in the large configuration space of possible partitionings on the one hand, or due to the actual presence of different classes of nodes on the other hand.

Multiple power-law structures in heterogeneous complex networks

This paper develops a framework for analyzing and designing dynamic networks comprising different classes of nodes that coexist and interact in one shared environment. We consider ad hoc (i.e., nodes can leave the network unannounced, and no node has any global knowledge about the class identities of other nodes) preferentially grown networks, where different classes of nodes are characterized by different sets of local parameters used in the stochastic dynamics that all nodes in the network execute. We show that multiple scale-free structures, one within each class of nodes, and with tunable power-law exponents (as determined by the sets of parameters characterizing each class), emerge naturally in our model. Moreover, the coexistence of the scale-free structures of the different classes of nodes can be captured by succinct phase diagrams, which show a rich set of structures, including stable regions where different classes coexist in heavy-tailed (i.e., the exponent is between 2 and 3) and light-tailed (i.e., the exponent is greater than 3) states, and sharp phase transitions. The topology of the emergent networks is also shown to display a complex structure, akin to the distribution of different components of an alloyed material; e.g., nodes with a light-tailed scale-free structure get embedded to the outside of the network, and have most of their edges connected to nodes belonging to the class with a heavy-tailed distribution. Finally, we show how the dynamics formulated in this paper will serve as an essential part of ad hoc networking protocols, which can lead to the formation of robust and efficiently searchable networks [including, the well-known peer-to-peer networks] even under very dynamic conditions.

DEAR: A Device and Energy Aware Routing protocol for heterogeneous ad hoc networks

Energy efficient routing in ad hoc networks has been the subject of intensive study in recent years. Most work has been focused on residual battery power related cost metrics for a homogeneous ad hoc network, where all the nodes are considered identical in terms of functioning and available resources. In this paper, we propose a Device and Energy Aware Routing protocol (DEAR) for a heterogeneous wireless ad hoc network where there exist two different classes of nodes, battery-powered nodes and externally powered nodes. By embedding both the energy and the device-type awareness into the routing protocol, we force the externally powered nodes to forward more traffic and perform more routing functions than a battery-powered node. The simulation results show that DEAR protocol achieves better system lifetime when compared to the conventional energy efficient routing protocols.

Motion planning with complete knowledge using a colored SOM.

The motion planning problem requires that a collision-free path be determined for a robot moving amidst a fixed set of obstacles. Most neural network approaches to this problem are for the situation in which only local knowledge about the configuration space is available. The main goal of the paper is to show that neural networks are also suitable tools in situations with complete knowledge of the configuration space. In this paper we present an approach that combines a neural network and deterministic techniques. We define a colored version of Kohonen's self-organizing map that consists of two different classes of nodes. The network is presented with random configurations of the robot and, from this information, it constructs a road map of possible motions in the work space. The map is a growing network, and different nodes are used to approximate boundaries of obstacles and the Voronoi diagram of the obstacles, respectively. In a second phase, the positions of the two kinds of nodes are combined to obtain the road map. In this way a number of typical problems with small obstacles and passages are avoided, and the required number of nodes for a given accuracy is within reasonable limits. This road map is searched to find a motion connecting the given source and goal configurations of the robot. The algorithm is simple and general; the only specific computation that is required is a check for intersection of two polygons. We implemented the algorithm for planar robots allowing both translation and rotation and experiments show that compared to conventional techniques it performs well, even for difficult motion planning scenes.

Efficient solutions to multicast routing in communication networks

An important problemin both wireless and wired communication networks is to be able to efficiently multicast information to a group of network sites. Multicasting reduces the transmission overhead of both wireless and wired networks and the time it takes for all the nodes in the subset to receive the information. Since transmission bandwidth is a scarce commodity especially in wireless networks, efficient and near minimum-cost multicast algorithms are particularly useful in the wireless context. In this paper, we discuss methods of establishing efficient and near minimum-cost multicast routing in communication networks. In particular, we discuss an efficient implementation of a widely used multicast routing method which can construct a multicast tree with a cost no greater than twice the cost of an optimal tree. We also present two efficient multicast tree constructions for a general version of the multicast routing problem in which a network consists of different classes of nodes, where each class can have one or more nodes of the same characteristic which is different fromthe characteristics of nodes fromother classes. Because of their efficient running times, these multicast routing methods are particularly useful in the mobile communication environments where topology changes will imply recomputation of the multicast trees. Furthermore, the proposed efficient and near minimum-cost multicast routing methods are particularly suited to the wireless communication environments, where transmission bandwidth is more scarce than wired communication environments.