Effect Of Network Size Research Articles

Predicting efficiency in master-slave grid computing systems

This work reports a quantitative analysis to predicting the efficiency of distributed computing running in three models of complex networks: Barabási–Albert, Erdos–Rényi and Watts–Strogatz. A master–slave computing model is simulated. A node is selected as master and distributes tasks among the other nodes (the clients). Topological measurements associated with the master node (e.g. its degree or [betwenness] betweenness centrality) are extracted and considered as predictors of the total execution time. It is found that the closeness centrality provides the best alternative. The effect of network size was also investigated.

Effects of supervised Self Organising Maps parameters on classification performance

Self Organising Maps (SOMs) are one of the most powerful learning strategies among neural networks algorithms. SOMs have several adaptable parameters and the selection of appropriate network architectures is required in order to make accurate predictions. The major disadvantage of SOMs is probably due to the network optimisation, since this procedure can be often time-expensive.Effects of network size, training epochs and learning rate on the classification performance of SOMs are known, whereas the effect of other parameters (type of SOMs, weights initialisation, training algorithm, topology and boundary conditions) are not so obvious.This study was addressed to analyse the effect of SOMs parameters on the network classification performance, as well as on their computational times, taking into consideration a significant number of real datasets, in order to achieve a comprehensive statistical comparison. Parameters were contemporaneously evaluated by means of an approach based on the design of experiments, which enabled the investigation of their interaction effects.Results highlighted the most important parameters which influence the classification performance and enabled the identification of the optimal settings, as well as the optimal architectures to reduce the computational time of SOMs.

Effects of sampling completeness on the structure of plant–pollinator networks

Plant-animal interaction networks provide important information on community organization. One of the most critical assumptions of network analysis is that the observed interaction patterns constitute an adequate sample of the set of interactions present in plant-animal communities. In spite of its importance, few studies have evaluated this assumption, and in consequence, there is no consensus on the sensitivity of network metrics to sampling methodological shortcomings. In this study we examined how variation in sampling completeness influences the estimation of six network metrics frequently used in the literature (connectance, nestedness, modularity, robustness to species loss, path length, and centralization). We analyzed data of 186 flowering plants and 336 pollinator species in 10 networks from a forest-fragmented system in central Chile. Using species-based accumulation curves, we estimated the deviation of network metrics in undersampled communities with respect to exhaustively sampled communities and the effect of network size and sampling evenness on network metrics. Our results indicate that: (1) most metrics were affected by sampling completeness but differed in their sensitivity to sampling effort; (2) nestedness, modularity, and robustness to species loss were less influenced by insufficient sampling than connectance, path length, and centralization; (3) robustness was mildly influenced by sampling evenness. These results caution studies that summarize information from databases with high, or unknown, heterogeneity in sampling effort per species and should stimulate researchers to report sampling intensity to standardize its effects in the search for broad patterns in plant-pollinator networks.

A study of sign adjustment in weighted signed networks

We analyse the adjustment of networks comprising of weighted positive (P) and negative (N) symmetric relations under the impact of various balancing rules. Five kinds of rules are studied: (1) a local minimal edge adjustment which is a special case of, (2) a local pressure based rule, (3) a local sign based rule, (4) a global rule and (5) rules varying on a local to global dimension. The convergence and convergent proportions of different 3-cycles and, thus the impact upon β(3) balance, under the different kinds of adjustment rule are studied both analytically and through simulation. The effects of network size (n), density (d) and the initial proportion of positive edges (α0) upon the convergence of 3-cycles and, thus, balance and the eventual implications for the process of group formation are explored.

Insights into the Relationships Among Capillary Pressure, Saturation, Interfacial Area and Relative Permeability Using Pore-Network Modeling

To gain insight in relationships among capillary pressure, interfacial area, saturation, and relative permeability in two-phase flow in porous media, we have developed two types of pore-network models. The first one, called tube model, has only one element type, namely pore throats. The second one is a sphere-and-tube model with both pore bodies and pore throats. We have shown that the two models produce distinctly different curves for capillary pressure and relative permeability. In particular, we find that the tube model cannot reproduce hysteresis. We have investigated some basic issues such as effect of network size, network dimension, and different trapping assumptions in the two networks. We have also obtained curves of fluid–fluid interfacial area versus saturation. We show that the trend of relationship between interfacial area and saturation is largely influenced by trapping assumptions. Through simulating primary and scanning drainage and imbibition cycles, we have generated two surfaces fitted to capillary pressure, saturation, and interfacial area (P c –S w –a nw ) points as well as to relative permeability, saturation, and interfacial area (k r –S w –a nw ) points. The two fitted three-dimensional surfaces show very good correlation with the data points. We have fitted two different surfaces to P c –S w –a nw points for drainage and imbibition separately. The two surfaces do not completely coincide. But, their mean absolute difference decreases with increasing overlap in the statistical distributions of pore bodies and pore throats. We have shown that interfacial area can be considered as an essential variable for diminishing or eliminating the hysteresis observed in capillary pressure–saturation (P c –S w ) and the relative permeability–saturation (k r –S w ) curves.

Delay jitter analysis of 802.11 DCF

A simple packet delay jitter analytical model is developed for wireless networks utilising IEEE 802.11 Distributed Coordination Function (DCF). The proposed model computes average packet delay and packet delay variability and is fairly accurate since analytical results closely match simulation outcome. The effect of network size and packet size on delay jitter is examined.

The effect of network size on intra-network knowledge processes

This paper addresses a neglected topic in the knowledge management literature: how the size of a network of actors affects the nature of intra-network social relations and knowledge processes. It makes a theoretical contribution to developing understanding in this area drawing on a range of literatures including practice-based perspectives on knowledge, the literature on the embeddedness of social relations, and relevant knowledge management literature. The central focus of this paper is on the relationship between network size, network density, and how these variables affect intra-network knowledge processes. It suggests that as network size increases network density is likely to decrease (as it becomes problematic for the actors in such networks to retain strong ties with a significant proportion of the network's members), which it will be suggested has significant ramifications for intra-network knowledge processes. This paper concludes by reflecting on the implications of the ideas developed for network-based forms of organizing, and innovation processes.

Ecological interface design and computer network management: The effects of network size and fault frequency

This article describes an experiment investigating the impact of ecological interface design (EID) on human performance in computer network management. This work domain is more dynamic than those previously studied under EID because there is a constant potential for the addition and removal of devices, as well as changing configurations, making it important to study the generalizability of the framework. Two interfaces were created for the University of Toronto campus network consisting of 220 nodes: a P interface based on existing design practices which presented primarily physical information and a P+F interface based on EID which presented both physical and functional information identified by an abstraction hierarchy analysis. Participants used one of the two interfaces to detect and diagnose faults or disturbances in the simulated network in real-time. Network size and fault frequency were both manipulated as within-participants variables. The P+F interface led to faster detection times overall, as well as improved fault detection rate and more accurate fault diagnosis under higher fault loads. These results suggest that the EID framework may lead to more robust monitoring performance in computer network management compared to existing interfaces.

Electron transport in nanocluster films with random voids

Nanocluster films are modeled as a network of tunnel junctions in which random voids have been introduced. The effects of network size and void distribution on electron transport are studied using Monte Carlo simulations of the Coulomb blockade mediated transport. The random void distributions of the model networks are systematically varied by randomly deleting junctions along each row and between the different rows. The nonlinearity and voltage threshold of the I-V curves are calculated for different void topologies and network sizes. Both the threshold voltage and nonlinearity are sensitive to lateral fluctuations of the conduction paths due to the random voids. The nonlinearity is found to be maximized for network aspect ratios of unity or larger and for particular network topologies. Both the nonlinearity and voltage threshold scale with network size. The behaviors seen in simulation are found to correspond well to Au nanocluster I-V measurements.

Neural Network Modeling for Estimation of the Aqueous Solubility of Structurally Related Drugs

The ability of neural network models to predict aqueous solubility within series of structurally related drugs was evaluated. Three sets of compounds representing different drug classes (28 steroids, 31 barbituric acid derivatives, and 24 heterocyclic reverse transcriptase inhibitors) were studied. Topological descriptors (connectivity indices, kappa indices, and electrotopological state indices) were used to link the structures of compounds with their aqueous solubility. Separate models were built for each class of drugs using back-propagation neural networks with one hidden layer and five topological indices as input parameters. The effect of network size and training time on the prediction ability of the network was studied by the leave-one-out (LOO) procedure. In all three compound groups a neural network structure of 5-3-1 was optimal. To avoid chance effects, artificial neural network (ANN) ensembles (i.e.; averaging neural network predictions over several independent networks) were used. The cross-validated squared correlation coefficient (Q2) for 10 averaged predictions was 0.796 in the case of the steroid set. The corresponding standard error of prediction (SDEP) was 0.288 log units. For the barbiturates, Q2 and SDEP were 0.856 and 0.383, respectively, and for the RT inhibitors, these parameters were 0.721 and 0.401, respectively. The results indicate that neural networks can produce useful models of the aqueous solubility of a congeneric set of compounds, even with simple structural parameters.

The effects of network size and fault intermittency on troubleshooting performance

The effects of network size and component test reliability (intermittency) on troubleshooting performance and strategy selection were investigated using a computer-based, fault-finding program (Networker). By testing system components and output devices, the subjects' task was to find the faulty component in a computer-based problem. Subjects were required to troubleshoot 18 problems. Results indicated that, as intermittency levels increased, problems became more difficult to troubleshoot. Unexpectedly, subject performance was less predictable as network size increased. As the problems increased from small- to medium-sized, subjects required more actions and more time to solve the problems. However, subjects performed more efficiently on the large-sized problems than they did on the medium-sized problems. Post-hoc examination indicated that other factors may have influenced complexity in addition to problem size.

Implicit memory: Effects of network size and interconnectivity on cued recall.

Implicit memory: Effects of network size and interconnectivity on cued recall.

Learning to Cooperate: Stochastic and Tacit Collusion in Social Exchange

The Prisoner's Dilemma formalizes the social trap that arises when individually rational choices aggregate with mutually undesirable consequences. The game-theoretic solution centers on the opportunity for tacit collusion in repeated play. However, not all actors grasp the strategic implications of future interaction. Accordingly, this study reformulates the game as a stochastic learning model in which the behavior of interdependent actors is constinually shaped by sanctions and cues generated by their interaction. Computer simulations of a two-person game show that adaptive actors are led into a social trap more readily than are fully rational actors, but they are also better at finding their way out. Prosocial norms appear to be a consequence rather than cause of cooperation but useful in promoting forgiveness of random deviance. The model is then elaborated as an N-way Prisoner's Dilemma. Simulations show how the effects of network size, density, mobility, and anonymity derive from a fundamental princi...

Problem Solving Performance of First Semester Maintenance Trainees in Two Fault Diagnosis Tasks

Forty-eight first semester trainees in an FAA certificate program participated in an experimental study of trouble-shooting of two different types of graphically displayed networks. The effects of network size, redundancy, feedback, computer aiding, and training were considered. It was found that performance degraded as network size increased, degraded as the level of feedback was reduced, improved with the use of computer aiding, and that skills developed with computer aiding in one task were transferred to the other task.

Problem Solving Performance of Maintenance Trainees in a Fault Diagnosis Task

Forty trainees in an FAA certificate program participated in an experimental study of trouble-shooting of graphically displayed networks. The effects of network size, computer aiding, and training were considered. It was found that performance degraded as network size increased, improved with the use of computer aiding, and that skills developed with computer aiding were transferred to the unaided situation.