727 research outputs found
One-step Estimation of Networked Population Size: Respondent-Driven Capture-Recapture with Anonymity
Population size estimates for hidden and hard-to-reach populations are
particularly important when members are known to suffer from disproportion
health issues or to pose health risks to the larger ambient population in which
they are embedded. Efforts to derive size estimates are often frustrated by a
range of factors that preclude conventional survey strategies, including social
stigma associated with group membership or members' involvement in illegal
activities.
This paper extends prior research on the problem of network population size
estimation, building on established survey/sampling methodologies commonly used
with hard-to-reach groups. Three novel one-step, network-based population size
estimators are presented, to be used in the context of uniform random sampling,
respondent-driven sampling, and when networks exhibit significant clustering
effects. Provably sufficient conditions for the consistency of these estimators
(in large configuration networks) are given. Simulation experiments across a
wide range of synthetic network topologies validate the performance of the
estimators, which are seen to perform well on a real-world location-based
social networking data set with significant clustering. Finally, the proposed
schemes are extended to allow them to be used in settings where participant
anonymity is required. Systematic experiments show favorable tradeoffs between
anonymity guarantees and estimator performance.
Taken together, we demonstrate that reasonable population estimates can be
derived from anonymous respondent driven samples of 250-750 individuals, within
ambient populations of 5,000-40,000. The method thus represents a novel and
cost-effective means for health planners and those agencies concerned with
health and disease surveillance to estimate the size of hidden populations.
Limitations and future work are discussed in the concluding section
On sampling social networking services
This article aims at summarizing the existing methods for sampling social
networking services and proposing a faster confidence interval for related
sampling methods. It also includes comparisons of common network sampling
techniques
Networks and the epidemiology of infectious disease
The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues
Corporate competition: A self-organized network
A substantial number of studies have extended the work on universal properties in physical systems to complex networks in social, biological, and technological systems. In this paper, we present a complex networks perspective on interfirm organizational networks by mapping, analyzing and modeling the spatial structure of a large interfirm competition network across a variety of sectors and industries within the United States. We propose two micro-dynamic models that are able to reproduce empirically observed characteristics of competition networks as a natural outcome of a minimal set of general mechanisms governing the formation of competition networks. Both models, which utilize different approaches yet apply common principles to network formation give comparable results. There is an asymmetry between companies that are considered competitors, and companies that consider others as their competitors. All companies only consider a small number of other companies as competitors; however, there are a few companies that are considered as competitors by many others. Geographically, the density of corporate headquarters strongly correlates with local population density, and the probability two firms are competitors declines with geographic distance. We construct these properties by growing a corporate network with competitive links using random incorporations modulated by population density and geographic distance. Our new analysis, methodology and empirical results are relevant to various phenomena of social and market behavior, and have implications to research fields such as economic geography, economic sociology, and regional economic development.Organizational networks; Interfirm competition; Economic geography; Social networks; Spatial networks; Network dynamics; Firm size dynamics
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