This study asks whether the spatial scale of sampling alters structural properties of food webs and whether any differences are attributable to changes in species richness and connectance with scale. Understanding how different aspects of sampling effort affect ecological network structure is important for both fundamental ecological knowledge and the application of network analysis in conserva...

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of varianc...

Determining the graph-theoretic properties of large real-world networks like social, computer, and biological networks, is a challenging task. Many of those networks are too large to be processed e ciently and some are not even available in their entirety. In order to reduce the size of available data or collect a sample of an existing network, several sampling algorithms were developed. They a...

Exactly computing network reliability and performability measures are NP-hard problems, precluding their frequent use in design of large networks. Instead, Monte Carlo simulation has been frequently used by network designers to obtain accurate estimates. This paper focuses on simulation estimation of network reliability and performability. First, a literature survey of existing approaches is gi...

Finding an optimal design for the environmental surveillance network is a realistic need for any ecosystem manager. There are two main factors related to the optimization of a surveillance network: number of sampling points (deciding the sampling density) and locations of such sampling points. This paper aims at proposing an agent-based model to add k measuring devices into a current surveillan...

network scale-up is an indirect method for estimating the size of hidden, hard-to-count or high risk populations. social network size estimation is the first step in this method. the present study was conducted with the purpose of estimating the social network size of the tehran province residents and its determinants.maximum likelihood estimation was applied to estimate people's network sizes ...

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of varianc...

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due ...

In this paper we discuss the design of sequential detection networks for nonparametric sequential analysis. We present a general probabilistic model for sequential detection problems where the sample size as well as the statistics of the sample can be varied. A general sequential detection network handles three decisions. First, the network decides whether to continue sampling or stop and make ...