Because of its complexity, reliability analysis of lifeline-network usually employs a sampling-based approach. MonteCarlo simulation (MCS) provides a straightforward method to deal with interdependence between structural components and their cascading failures in the lifeline network system, but its computational cost might be expensive if the probability of the event of interest is too low. To...

Lately, network sampling proved as a promising tool for simplifying large real-world networks and thus providing for their faster and more efficient analysis. Still, understanding the changes of network structure and properties under different sampling methods remains incomplete. In this paper, we analyze the presence of characteristic group of nodes (i.e., communities, modules and mixtures of ...

Sampling from Large Scale Social Networks is a hot topic in recent research. In telecommunications services, there are many networks with millions of nodes and billions of edges. They are complex and difficult to analyze. Sampling, together with vizualization techniques, are required for exploratory data analysis and event detection. Until now, to visualize and analyze the massive network data ...

in the present study, through the application of self-determination theory the impact of work climate and social network size on the volunteers motivation were investigated. research type of this study is applied and research method type is descriptive. in this study active volunteers of tehran red crescent society were selected as statistical population .concerning the population of studied ce...

The robustness and integrity of IP networks require efficient tools for traffic monitoring and analysis, which scale well with traffic volume and network size. We address the problem of optimal large-scale flow monitoring of computer networks under resource constraints. We propose a stochastic optimization framework where traffic measurements are done by exploiting the spatial (across network l...

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORK...

Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networks (i) where we have one attribute with two values (e.g., male and female in th...

We develop a novel Bayesian nonparametric model for random bipartite graphs. The model is based on the theory of completely random measures and is able to handle a potentially infinite number of nodes. We show that the model has appealing properties and in particular it may exhibit a power-law behavior. We derive a posterior characterization, a generative process for network growth, and a simpl...