Networks Of Different Sizes Research Articles

Moment-based travel time reliability assessment with Lasserre’s relaxation

ABSTRACTThis paper proposes the generalized moment problem (GMP) to assess total system travel time (TSTT) reliability on a traffic network. As a distribution-free method, the proposed approach only needs moment information of uncertainty to make the evaluation. In the GMP, upper bound of the probability that TSTT exceeds some predefined threshold value (which is called unreliability function) is the objective, and moment information of uncertainty is formulated as constraints. Based on the first moments and support information of total link travel time of each link, we use Lasserre’s relaxation to reformulate the GMP as a semi-definite programming, which can be solved efficiently. We test the proposed method on traffic networks of different sizes. Numerical results show that the upper bounds obtained with our method are tightest in most cases and show different tail information. The results may also bring some useful insight to applications of the moment-based method in practice.

Energy-Efficient Service Function Chain Provisioning

Network function virtualization (NFV) is a promising network architecture concept to reduce operational costs. In legacy networks, network functions, such as firewall or TCP optimization, are performed by specific hardware. In networks enabling NFV coupled with the software defined network (SDN) paradigm, virtual network functions (VNFs) can be implemented dynamically on generic hardware. This is of primary interest to implement energy-efficient solutions, in order to adapt the resource usage dynamically to the demand. In this paper, we study how to use NFV coupled with an SDN to improve the energy efficiency of networks.We consider a setting inwhich a flowhas to go through a service function chain, which is several network functions in a specific order.We propose an integer linear programming (ILP) formulation, an ILP-based heuristic, and a decomposition model that relies on joint routing and placement configuration to solve the problem. We show that virtualization provides between 22% and 62% of energy savings for networks of different sizes.

Energy-Efficient Service Function Chain Provisioning

Network Function Virtualization (NFV) is a promising network architecture concept to reduce operational costs. In legacy networks, network functions, such as firewall or TCP optimization, are performed by specific hardware. In networks enabling NFV coupled with the Software Defined Network (SDN) paradigm, network functions can be implemented dynamically on generic hardware. This is of primary interest to implement energy efficient solutions, in order to adapt dynamically the resource usage to the demands. In this paper, we study how to use NFV coupled with SDN to improve the energy efficiency of networks. We consider a setting in which a flow has to go through a Service Function Chain, that is several network functions in a specific order. We propose a decomposition model that relies on chaining and function placement configurations to solve the problem. We show that virtualization allows to obtain between 15% to 62% of energy savings for networks of different sizes.

Stochastic dynamic programming approach to managing power system uncertainty with distributed storage

Wind integration in power grids is challenging because of the uncertain nature of wind speed. Forecasting errors may have costly consequences. Indeed, power might be purchased at highest prices to meet the load, and in case of surplus, power may be wasted. Energy storage may provide some recourse against the uncertainty of wind generation. Because of their sequential nature, in theory, power scheduling problems may be solved via stochastic dynamic programming. However, this scheme is limited to small networks by the so-called curse of dimensionality. This paper analyzes the management of a network composed of conventional power units and wind turbines through approximate dynamic programming, more precisely stochastic dual dynamic programming. A general power network model with ramping constraints on the conventional generators is considered. The approximate method is tested on several networks of different sizes. The numerical experiments also include comparisons with classical dynamic programming on a small network. The results show that the combination of approximation techniques enables to solve the problem in reasonable time.

Accelerated Distributed Optimization Design for Reconstruction of Big Sensory Data

According to the practical requirements of high recovery precision and low latency in wireless big sensory data networks, this paper proposes an accelerated distributed rate control method for minimizing the recovery error of big sensory data. This method can guarantee the error minimization of reconstructed data and converge to the optimal value fast with a lower latency. In order to achieve these effects, an accelerated distributed solving algorithm is constructed by designing accelerated subgradient method for dual decomposition. This solving algorithm achieves convergence rate ${O(1/{t^{2}})}$ in practical implementation, which significantly improves the convergence rate of regular solving algorithms. Meanwhile, the convergence analysis testifies the convergence property of the proposed distributed solving algorithm, and this algorithm is applicable to other convex optimization problems. Finally, the performance evaluation shows that the proposed accelerated method can converge to the unique optimal value successfully and the convergence speed is faster than the regular optimization method, and this proposed method can be extended to networks of different sizes without sacrificing the accelerated effect.

Adaptive community detection in complex networks using genetic algorithms

Community detection is a challenging optimisation problem that consists in searching for communities that belong to a network or graph under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. A large number of methods have been proposed to address this problem in many research fields, such as power systems, biology, sociology or physics. Many of those optimisation methods use modularity to identify the optimal network subdivision. This paper presents a new generational genetic algorithm (GGA+) that includes efficient initialisation methods and search operators under the guidance of modularity. Further, this approach enables a flexible and adaptive analysis of the characteristics of a network from different levels of detail according to an analyst’s needs. Results obtained in networks of different sizes and characteristics show the good performance of GGA+ in comparison with other five genetic algorithms, including efficient algorithms published in recent years.

Fractal: An advanced multidimensional range query lookup protocol on nested rings for distributed systems

One of the key issues in large-scale distributed systems such as P2P and grids is the capability of efficient multidimensional range query processing. Although several methods have recently been proposed for solving this problem on distributed systems, these methods have not been able to meet the fundamental necessity of a typical method on large-scale distributed systems, i.e., scalability.This paper presents Fractal, a fully decentralized and highly scalable multidimensional range query lookup protocol for distributed systems. In this work, to organize the available nodes in the system, an n-dimensional space called Key Space is utilized. The available nodes on the nested Fractal rings maintain information about only O(logN) other nodes, and the Fractal lookup protocol discovers the destination node through these nested rings with a logarithmic cost. Because of its flexibility, Fractal allows the system to create a concept called Layering, which minimizes the probability of the wide-area message transfers (WAMTs) during the lookup process. Using several criteria, Fractal is compared with several successful methods that have recently been presented. Simulation results show the efficiency and performance of Fractal in networks of different sizes.

Research on contaminant sources identification of uncertainty water demand using genetic algorithm

Urban water supply network is easily affected by intentional or occasional chemical and biological pollution, which threatens the health of consumers. In recent years, drinking water contamination happens occasionally, which seriously harms social stabilization and safety. Placing sensors in water supply pipes can monitor water quality in real time, which may prevent contamination accidents. However, how to reversely locate pollution sources through the detecting information from water quality sensors is a challengeable issue. Its difficulties lie in that limited sensors, massive pipe network nodes and dynamic water demand of users lead to the uncertainty, large-scale and dynamism of this optimization problem. This paper mainly studies the uncertainty problem in contaminant sources identification (CSI). The previous study of CSI supposes that hydraulic output (e.g., water demand) is known. Whereas, the inherent variability of urban water consumption brings an uncertain problem that water demand presents volatility. In this paper, the water demand of water supply network nodes simulated by Gaussian model is stochastic, and then being used to solve the problem of CSI, simulation–optimization method finds the minimum target of CSI and concentration which meet the simulation value and detected value of sensors. This paper proposes an improved genetic algorithm to solve the CSI problem under uncertainty water demand and comparative experiments are placed on two water distribution networks of different sizes.

Comparison of Different Generalizations of Clustering Coefficient and Local Efficiency for Weighted Undirected Graphs.

Binary undirected graphs are well established, but when these graphs are constructed, often a threshold is applied to a parameter describing the connection between two nodes. Therefore, the use of weighted graphs is more appropriate. In this work, we focus on weighted undirected graphs. This implies that we have to incorporate edge weights in the graph measures, which require generalizations of common graph metrics. After reviewing existing generalizations of the clustering coefficient and the local efficiency, we proposed new generalizations for these graph measures. To be able to compare different generalizations, a number of essential and useful properties were defined that ideally should be satisfied. We applied the generalizations to two real-world networks of different sizes. As a result, we found that not all existing generalizations satisfy all essential properties. Furthermore, we determined the best generalization for the clustering coefficient and local efficiency based on their properties and the performance when applied to two networks. We found that the best generalization of the clustering coefficient is [Formula: see text], defined in Miyajima and Sakuragawa ( 2014 ), while the best generalization of the local efficiency is [Formula: see text], proposed in this letter. Depending on the application and the relative importance of sensitivity and robustness to noise, other generalizations may be selected on the basis of the properties investigated in this letter.

A Line Graph-Based Continuous Range Query Method for Moving Objects in Networks

The rapid growth of location-based services has motivated the development of continuous range queries in networks. Existing query algorithms usually adopt an expansion tree to reuse the previous query results to get better efficiency. However, the high maintenance costs of the traditional expansion tree lead to a sharp efficiency decrease. In this paper, we propose a line graph-based continuous range (LGCR) query algorithm for moving objects in networks, which is characterized by a novel graph-based expansion tree (GET) structure used to monitor queries in an incremental manner. In particular, GET is developed based on the line graph model of networks and simultaneously supports offline pre-computation to better adapt our proposed algorithm to different sizes of networks. To improve performance, we create a series of related data structures, such as bridgeable edges and distance edges. Correspondingly, we develop several algorithms, including initialization, insertion of objects, filter and refinement and location update, to incrementally re-evaluate continuous range queries. Finally, we implement the GET and related algorithms in the native graph database Neo4J. We conduct experiments using real-world networks and simulated moving objects and compare the proposed LGCR with the existing classical algorithm to verify its effectiveness and demonstrate its greater efficiency.

Finding network communities using modularity density

Many real-world complex networks exhibit a community structure, in which the modules correspond to actual functional units. Identifying these communities is a key challenge for scientists. A common approach is to search for the network partition that maximizes a quality function. Here, we present a detailed analysis of a recently proposed function, namely modularity density. We show that it does not incur in the drawbacks suffered by traditional modularity, and that it can identify networks without ground-truth community structure, deriving its analytical dependence on link density in generic random graphs. In addition, we show that modularity density allows an easy comparison between networks of different sizes, and we also present some limitations that methods based on modularity density may suffer from. Finally, we introduce an efficient, quadratic community detection algorithm based on modularity density maximization, validating its accuracy against theoretical predictions and on a set of benchmark networks.

Cost-Optimal Time-dEpendent Routing with Time and Speed Constraints in Directed Acyclic Road Networks

Travel costs on road networks always change over time which implies road networks are time dependent. Most studies on time-dependent road networks simply find the shortest path with the least travel time without considering waiting at some nodes, or fuel consumption and toll fee. In real-world applications or computer games, waiting may be allowed at some nodes but disallowed at other nodes; a user can traverse an edge at different speeds; monetary travel cost contains fuel cost and toll fees; and users usually prefer the minimum-cost route under time and speed constraints. Therefore, we study Cost-Optimal Time-dEpendent Routing (COTER) problem with time and speed constraints. We utilize two fuel consumption models and compute the minimum fuel consumption with given travel time for highway edges via nonlinear optimization. We allow the toll fee function to be an arbitrary single-valued time-dependent function. We define an Optimal Cost (OC) function for each candidate node [Formula: see text], and derive the recurrence relation formula between [Formula: see text]’s incoming neighbors’ OC-functions and [Formula: see text]’s OC-functions. To solve COTER, we propose a five-step algorithm, namely, ALG-COTER, which uses Fibonacci-heap optimized Dijkstra, topological sorting, dynamic programming, binary min-heap optimization, nonlinear optimization, and backtracking algorithms. Experimental results on three real-world road networks of different sizes demonstrate that our algorithm finds the optimal route efficiently and is scalable to different parameters.

Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.

Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.

Relaxation dynamics of small-world degree-distributed treelike polymer networks

Abstract Hyperbranched polymers are typically treelike macromolecules with a very disordered structure. Here we construct hyperbranched polymers based on the degree distribution of the small-world networks. This algorithm allows us to study a transition from monodisperse linear chains to structurally-disordered dendritic polymers by varying the parameter p ( 0 ≤ p ≤ 1 ), which measures the randomness and the degree of branching of the network. Employing the framework of generalized Gaussian structures, we determine for the obtained structures the relaxation spectra, which are exemplified on the mechanical relaxation moduli (storage and loss moduli). We monitor these physical quantities for networks of different sizes and for various values of the parameter p . In the intermediate frequency domain, we encounter macroscopically distinguishable behaviours.

Noise influence on spike activation in a Hindmarsh–Rose small-world neural network

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator xo feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator xo has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh−Rose (H−R) neural networks.

Empirical reference distributions for networks of different size

Network analysis has become an increasingly prevalent research tool across a vast range of scientific fields. Here, we focus on the particular issue of comparing network statistics, i.e. graph-level measures of network structural features, across multiple networks that differ in size. Although “normalized” versions of some network statistics exist, we demonstrate via simulation why direct comparison is often inappropriate. We consider normalizing network statistics relative to a simple fully parameterized reference distribution and demonstrate via simulation how this is an improvement over direct comparison, but still sometimes problematic. We propose a new adjustment method based on a reference distribution constructed as a mixture model of random graphs which reflect the dependence structure exhibited in the observed networks. We show that using simple Bernoulli models as mixture components in this reference distribution can provide adjusted network statistics that are relatively comparable across different network sizes but still describe interesting features of networks, and that this can be accomplished at relatively low computational expense. Finally, we apply this methodology to a collection of ecological networks derived from the Los Angeles Family and Neighborhood Survey activity location data.

A generative modeling approach to connectivity—Electrical conduction in vascular networks

The physiology of biological structures is inherently dynamic and emerges from the interaction and assembly of large collections of small entities. The extent of coupled entities complicates modeling and increases computational load. Here, microvascular networks are used to present a novel generative approach to connectivity based on the observation that biological organization is hierarchical and composed of a limited set of building blocks, i.e. a vascular network consists of blood vessels which in turn are composed by one or more cell types. Fast electrical communication is crucial to synchronize vessel tone across the vast distances within a network. We hypothesize that electrical conduction capacity is delimited by the size of vascular structures and connectivity of the network. Generation and simulation of series of dynamical models of electrical spread within vascular networks of different size and composition showed that (1) Conduction is enhanced in models harboring long and thin endothelial cells that couple preferentially along the longitudinal axis. (2) Conduction across a branch point depends on endothelial connectivity between branches. (3) Low connectivity sub-networks are more sensitive to electrical perturbations. In summary, the capacity for electrical signaling in microvascular networks is strongly shaped by the morphology and connectivity of vascular (particularly endothelial) cells. While the presented software can be used by itself or as a starting point for more sophisticated models of vascular dynamics, the generative approach can be applied to other biological systems, e.g. nervous tissue, the lymphatics, or the biliary system.

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.

Reputation Aggregation in Peer-to-Peer Network Using Differential Gossip Algorithm

Reputation aggregation in peer to peer networks is generally a very time and resource consuming process. Moreover, most of the methods consider that a node will have same reputation with all the nodes in the network, which is not true. This paper proposes a reputation aggregation algorithm that uses a variant of gossip algorithm called differential gossip. In this paper, estimate of reputation is considered to be having two parts, one common component which is same with every node, and the other one is information received from immediate neighbours based on the neighbours' direct interaction with the node. The differential gossip is fast and requires less amount of resources. This mechanism allows computation of independent reputation value by a node, of every other node in the network, for each node. The differential gossip trust has been investigated for a power law network formed using preferential attachment \emph{(PA)} Model. The reputation computed using differential gossip trust shows good amount of immunity to the collusion. We have verified the performance of the algorithm on the power law networks of different sizes ranging from 100 nodes to 50,000 nodes.

Abstracts

Hyperbranched polymers are typically treelike macromolecules with a very disordered structure. Here we construct hyperbranched polymers based on the degree distribution of the small-world networks. This algorithm allows us to study a transition from monodisperse linear chains to structurally-disordered dendritic polymers by varying the parameter p (0≤p≤1), which measures the randomness and the degree of branching of the network. Employing the framework of generalized Gaussian structures, we determine for the obtained structures the relaxation spectra, which are exemplified on the mechanical relaxation moduli (storage and loss moduli). We monitor these physical quantities for networks of different sizes and for various values of the parameter p. In the intermediate frequency domain, we encounter macroscopically distinguishable behaviours.