Medium-sized Networks Research Articles

A Comparative Study of Different Approaches for Finding the Upper Boundary Points in Stochastic-Flow Networks

An information system network (ISN) can be modeled as a stochastic-flow network (SFN). There are several algorithms to evaluate reliability of an SFN in terms of Minimal Cuts (MCs). The existing algorithms commonly first find all the upper boundary points (called d-MCs) in an SFN, and then determine the reliability of the network using some approaches such as inclusion-exclusion method, sum of disjoint products, etc. However, most of the algorithms have been compared via complexity results or through one or two benchmark networks. Thus, comparing those algorithms through random test problems can be desired. Here, the authors first state a simple improved algorithm. Then, by generating a number of random test problems and implementing the algorithms in MATLAB, the proposed algorithm is demonstrated to be more efficient than some existing ones in medium-sized networks. The performance profile introduced by Dolan and More is used for analyzing the output of programs.

Analysis of directed networks via partial singular value decomposition and Gauss quadrature

Large-scale networks arise in many applications. It is often of interest to be able to identify the most important nodes of a network or to determine the ease of traveling between them. We are interested in carrying out these tasks for directed networks. These networks have a nonsymmetric adjacency matrix A. Benzi et al. [6] recently proposed that these tasks can be accomplished by studying certain matrix functions, such as hyperbolic cosine and sine, of ATA and AAT. For small to medium-sized networks, the required computations can be easily carried out by first computing the singular value decomposition of A. However, for large networks this is impractical. We propose to first compute a partial singular value decomposition of A, which allows us to determine a subset of nodes that contains the most important nodes or a subset of nodes between which it is easy to travel. We then apply Gauss quadrature to rank the nodes in these subsets. Several computed examples illustrate the performance of the approach proposed.

Time-Based Toll Design for a Cordon-Based Congestion Pricing Scheme for a Transportation Network with Speed Limits and Movement Prohibitions

In the central business district (CBD) of metropolises, speed limits and movement prohibitions are often imposed on roads and intersections, respectively, for the purpose of safety and congestion mitigation and sometimes also to attempt to cut down vehicular aggregations and emissions. Based on this kind of general transportation network, this paper investigates the optimal time-based toll design problem for a cordon-based congestion pricing scheme in which toll charges are determined according to time consumed by travelers in traversing a predetermined cordon. Under the common hypothesis of travelers following the deterministic user equilibrium principle, this practical time-based toll design problem is formulated as a mathematical programming with equilibrium constraint (MPEC) model and solved by the Hooke-Jeeves algorithm, a derivative-free pattern search method. Taking into account the movement prohibitions at intersections, this paper presents a shortest-path model with movement prohibitions and develops an efficient branch and bound algorithm. Subsequently, the deterministic user equilibrium model can be solved by combining the proposed branch and bound algorithm with the method of successive averages (MSA). Finally, a medium-sized network example is adopted to numerically validate the proposed models and algorithms, and different features and results of four kinds of toll design schemes are further analyzed and discussed.

Research of Edge Centrality Based on the Algebraic Connectivity

In connected graph, edge centrality represents the importance of edge and loading degree in the process of information transmission. The second eigenvalue of Laplacian matrix decides connectivity of complex networks. We propose edge centrality model and cut model based on minimization model of the algebraic connectivity. Edge centrality function is derived in order to calculate edge centrality. Cut model deletes k edges at an iteration whose algebraic connectivity of complex networks decreased fastest. Choice of k is based on edge sparse degree of complex networks. By empirical analysis of real network, the implications of edge with higher centrality and its vertex coincide with the fact. The model can be used in medium-size networks with lower time complexity and higher efficiency.

Turning restriction design in traffic networks with a budget constraint

The restriction (prohibition) of certain turns at intersections is a very common task employed by the managers of urban traffic networks. Surprisingly, this approach has received little attention in the research literature. The turning restriction design problem (TRDP) involves finding a set of turning restrictions at intersections to promote flow in a congested urban traffic network. This article uses a successive linear approximation (SLA) method for identifying approximate solutions to a nonlinear model of the TRDP. It aims to adjust the current turning restriction regime in a given network in order to minimize total user travel cost when route choice is driven by user equilibrium principles. Novel features of the method include the facts that it is based on link capacity-based arc travel costs and there is a budget constraint on the total cost of all turning restriction alterations. It has been tested using standard network examples from the literature. One of the tests utilized a multi-start approach which improved the solutions produced by the SLA method. The method was also employed to identify turning restrictions for an actual medium-sized urban traffic network in Brazil. Computational experience with the proposed method is promising.

Transit network design based on travel time reliability

This paper presents a transit network optimization method, in which travel time reliability on road is considered. A robust optimization model, taking into account the stochastic travel time, is formulated to satisfy the demand of passengers and provide reliable transit service. The optimization model aims to maximize the efficiency of passenger trips in the optimized transit network. Tabu search algorithm is defined and implemented to solve the problem. Then, transit network optimization method proposed in this paper is tested with two numerical examples: a simple route and a medium-size network. The results show the proposed method can effectively improve the reliability of a transit network and reduce the travel time of passengers in general.

Modeling the Transport of Physicochemical Parameters for Water Network Security

Water quality of water distribution networks may be assessed using models. Single-species transport-reaction equations without interaction between indicators are often sufficient to reproduce the observed values. The objective of this paper is to present a new numerical scheme, which transforms water quality equations into a unique simple transport equation to solve. An advection-reaction equation is derived from incompressible Navier-Stokes for modeling the water temperature in the network. A quality solver was realized using Matlab and validated on small and medium-sized networks. As a result the same subroutine code is called for calculating the evolution of several water quality indicators.

SurpriseMe: an integrated tool for network community structure characterization using Surprise maximization

Detecting communities and densely connected groups may contribute to unravel the underlying relationships among the units present in diverse biological networks (e.g. interactomes, coexpression networks, ecological networks). We recently showed that communities can be precisely characterized by maximizing Surprise, a global network parameter. Here, we present SurpriseMe, a tool that integrates the outputs of seven of the best algorithms available to estimate the maximum Surprise value. SurpriseMe also generates distance matrices that allow visualizing the relationships among the solutions generated by the algorithms. We show that the communities present in small- and medium-sized networks, with up to 10 000 nodes, can be easily characterized: on standard PC computers, these analyses take less than an hour. Also, four of the algorithms may rapidly analyze networks with up to 100 000 nodes, given enough memory resources. Because of its performance and simplicity, SurpriseMe is a reference tool for community structure characterization. SurpriseMe is implemented in Perl and C/C++. It compiles and runs on any UNIX-based operating system, including Linux and Mac OS/X, using standard libraries. The source code is freely and publicly available under the GPL 3.0 license at http://github.com/raldecoa/SurpriseMe/releases.

A smart local moving algorithm for large-scale modularity-based community detection

We introduce a new algorithm for modularity-based community detection in large networks. The algorithm, which we refer to as a smart local moving algorithm, takes advantage of a well-known local moving heuristic that is also used by other algorithms. Compared with these other algorithms, our proposed algorithm uses the local moving heuristic in a more sophisticated way. Based on an analysis of a diverse set of networks, we show that our smart local moving algorithm identifies community structures with higher modularity values than other algorithms for large-scale modularity optimization, among which the popular “Louvain algorithm”. The computational efficiency of our algorithm makes it possible to perform community detection in networks with tens of millions of nodes and hundreds of millions of edges. Our smart local moving algorithm also performs well in small and medium-sized networks. In short computing times, it identifies community structures with modularity values equally high as, or almost as high as, the highest values reported in the literature, and sometimes even higher than the highest values found in the literature.

Computational precision of traffic equilibria sensitivities in automatic network design and road pricing

Recent studies demonstrate the importance of computational precision of user equilibrium traffic assignment solutions for scenario comparisons. When traffic assignment is hierarchically embedded in a model for network design and/or road pricing, not only the precision of the solution itself becomes more important, but also the precision of its derivatives with respect to the design parameters should be considered.The main purpose of this paper is to present a method for precise computations of equilibrium derivatives. Numerical experiments are used for two evaluations: (1) precision of computed equilibrium derivatives for a medium-size network (Anaheim); and (2) the impact of precise derivatives on capacity-expansion solution quality for a small network (Sioux Falls).

Fast layout computation of clustered networks: Algorithmic advances and experimental analysis

The visual analysis of large and complex relational data sets is a growing need in many application domains, such as social sciences, biology, computer networks, and software engineering. In this respect, the capability of quickly computing two-dimensional layouts of hierarchically clustered networks plays an important role and should be a major requirement of many graph visualization systems. We present algorithmic and experimental advances on the subject, namely: (i) we propose a new drawing algorithm that combines space-filling and fast force-directed methods; it runs in O(n logn+m) time, where n and m are the number of vertices and edges of the network, respectively. This running time does not depend on the number of clusters, thus the algorithm guarantees good time performance independently of the structure of the cluster hierarchy. As a further advantage, the algorithm can be easily parallelized. (ii) We discuss the results of an experimental analysis aimed at understanding which clustering algorithms can be used in combination with our visualization technique to generate better quality drawings for small-world and scale-free networks of medium and large size. As far as we know, no previous similar experiments have been done to this aim.

Robust FIPP p-cycles against dual link failures

We propose a new generic flow formulation for Failure-Independent Path-Protecting (FIPP) p-cycles subject to multiple failures. While our new model resembles the decomposition model formulation proposed by Orlowski and Pioro (Networks, 2011) in the case of classical shared path protection, its originality lies in its adaptation to FIPP p-cycles. When adapted to that last pre-configured pre-cross connected protection scheme, the bandwidth sharing constraints must be handled in a different way in order to take care of the sharing along the FIPP p-cycles. It follows that, instead of a polynomial-time solvable pricing problem as in the model of Orlowski and Pioro (Networks, 2011), we end up with a much more complex pricing problem, which has an exponential number of constraints due to some subtour elimination constraints. Consequently, in order to efficiently solve the pricing problem, we consider: (i) a hierarchical decomposition of the original pricing problem; (ii) heuristics in order to go around the large number of constraints in the pricing problem. Performance evaluation is made in the case of FIPP p-cycles subject to dual failures. For small to medium size networks, the proposed model remains fairly scalable for increasing percentages of dual failures, and requires much less bandwidth than p-cycle protection schemes (ratio varies from 2 to 4). For larger networks, heuristics are required in order to keep computing times reasonable. In the particular case of single link failures, it compares very favorably (5 to 10 % of bandwidth saving) to the previously proposed column generation ILP model of Rocha, Jaumard and Stidsen (Telecommun. Syst., 2012).

Adjustment of the link travel-time functions in traffic equilibrium assignment models

The network calibration problem (NCP) consists of adjusting (or estimating) the parameters of the link travel-time functions in a congested traffic network via the use of traffic link counts and equilibrium costs in a set of origin–destination demand pairs. In this article, a mathematical program with equilibrium constraints (MPEC) is proposed to model this problem. The proposed model is called NCP(ρ) and supposes that the available information has been obtained in several different periods (each period is defined by a single origin–destination demand matrix). The existence of the solutions to the stated NCP(ρ) under weak conditions in the link travel-time functions is established, and a sensitivity analysis of these solutions is carried out. A column generation algorithm (CGA) is proposed to solve the NCP(ρ) model, using sensitivity analysis to prove that this algorithm converges to a local optimum of the MPEC problem. Finally, the special case of estimating the link travel-time functions in additive models when information is only available for a single period is analysed in a numerical study. In this case the NCP(ρ) model is overspecified, that is, it has an infinite number of solutions. For this reason we propose, as an alternative, estimating the congestion costs ψ in a subset of links, rather than estimating the parameterisation of the link travel-time functions ρ. This special case has been named the NCP(ψ) model. The numerical study has been carried out in small and medium-sized networks, with the objective of studying both the proposed MPEC models and the computational aspects of the CGA.

Energy-Aware Design of Multilayer Core Networks [Invited

We consider the power-efficient design of an Internet protocol (IP)-over-wavelength division multiplexing (WDM) network, tackling the problem of installing optical and IP layer equipment to satisfy traffic requirements. We take into account routing constraints and consider a comprehensive set of realistic scenarios defined by network topology, traffic matrix, and power consumption values of network devices in both layers. Furthermore, besides defining and solving an optimal integer linear programming model, we propose an efficient heuristic to solve the problem up to medium-sized networks. The proposed heuristic requires at most 30% of additional power with respect to the optimal solution, but with a significantly reduced complexity. We show that the largest power consumption is due to line cards and routers rather than WDM equipment. Furthermore, we find that multipath routing reduces the network power consumption with respect to single-path routing, but not significantly. Finally, we show that a two-step design procedure, in which power is separately minimized in each layer (i.e., IP and WDM layers), can find results with a total power consumption comparable to the one achieved by a more complex joint multilayer design procedure.

A Study on Approximation Performances of Improved Bp Neural Networks Based on LM Algorithms

When approximating nonlinear functions, standard BP algorithms and traditional improved BP algorithms have low convergence rate and tend to be stuck in local minimums. In this paper, standard BP algorithm is improved by numerical optimization algorithm. Firstly, the principle of Levenberg-Marquardt algorithm is introduced. Secondly, to test its approximation performance, LMBP neural network is programmed via MATLAB7.0 taking specific nonlinear function as an example. Thirdly, its approximation result is compared with those of standard BP algorithm and adaptive learning rate algorithm. Simulation results indicate that compared with standard BP algorithm and adaptive learning rate algorithm, LMBP algorithm overcomes deficiencies ranging from poor convergence ability, prolonged convergence time, increasing iteration steps to nonconvergence. Thus with its good approximation ability, LMBP algorithm is the most suitable for medium-sized networks.

A Harmony Search Algorithm approach for optimizing traffic signal timings

In this study, a bi-level formulation is presented for solving the Equilibrium Network Design Problem (ENDP). The optimisation of the signal timing has been carried out at the upper-level using the Harmony Search Algorithm (HSA), whilst the traffic assignment has been carried out through the Path Flow Estimator (PFE) at the lower level. The results of HSA have been first compared with those obtained using the Genetic Algorithm, and the Hill Climbing on a two-junction network for a fixed set of link flows. Secondly, the HSA with PFE has been applied to the medium-sized network to show the applicability of the proposed algorithm in solving the ENDP. Additionally, in order to test the sensitivity of perceived travel time error, we have used the HSA with PFE with various level of perceived travel time. The results showed that the proposed method is quite simple and efficient in solving the ENDP.

Network Optimization Problems Subject to Max-Min Fair Flow Allocation

We propose a novel way to consider the max-min fairness (MMF) paradigm in traffic engineering. Since MMF appears as a reference model for a fair capacity allocation when the traffic flows are elastic and rates are adapted based on resource availability, we consider it as a requirement due to the way resources are shared by the distributed rate control scheme (like that of the transport protocol), rather than the routing objective. In particular, we define the traffic engineering problem where, given a network topology with link capacities and a set of elastic traffic demands to route, we must select a single path for each demand so as to maximize a network utility function, assuming an MMF bandwidth allocation. We propose a compact mixed-integer linear programming formulation as well as a restricted path formulation. We show with computational experiments that the exact formulation can be solved in a reasonable amount of computing time for medium-size networks and that the restricted path model provides solutions of comparable quality much faster.

A current based model for load flow studies with UPFC

This paper deals with an alternative proposition for the steady state modeling of unified power flow controller (UPFC). Since current limitations are determinant to FACTS apparatus design, the proposed current based model (CBM) assumes the current as variable, allowing easy manipulation of current restrictions in optimal power flow evaluations. The performance of the proposed model and of the power injection model (PIM) are compared through a Quasi-Newton optimization approach. Two operating situations of a medium size network with 39 busbars were studied from the point of view of optimization and current limits, observing the performance of the UPFC modeling.

Cutting edge or cut loose? An exploration of apprentices’ experiences of workplace e-learning

There is an assumption that e-learning is considered as a valued resource with great potential. This research explores how hairdressing apprentices experience vocational e-learning and makes recommendations on how good practice can be shared. An estimated 35,000 hairdressing and barber salons account for a quarter of a million UK workers and training is predominantly work-based. This small-scale, collaborative, practitioner-based research took place in a medium-sized salon network based in the East Midlands of England and employed a cross-sectional design. Salons were equipped with and use information and communication technology. Data collection included focus groups and participant diaries. Participation was voluntary and confidentiality maintained. Participants were allocated to form three focus groups differentiated by role and location. Participants kept a two week diary. Thematic, substantive, cross-sectional analysis was used to elicit data from the focus group transcripts and diaries. Findings, authenticated and validated by participants, indicated that e-learning was perceived as an overall positive resource in supporting workplace learning. It provided a means of ‘building bridges’ with senior staff and accessing expansive learning opportunities. Where accessibility was limited and production values dominated learning, apprentices experienced difficulties.

Useful science of selected eclipsing binaries with small to medium-size telescopes network

In the era of staggering Kepler data and sophisticated approach of the automatic analysis, how obsolete are the traditional object-by-object multiwavelength photometric observations? Can we apply the new tools of classification, light curve modeling and timing analysis to study the newly detected or/and most interesting EBs or to detect circumbinary bodies? In this paper, we present the contribution of “Mythodea” Observatory University of Patras to DWARF project that promises additional useful science of binary stars within an extensive network of relatively small to medium-size telescopes with apertures of ∼20–200 cm.