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Heckathorn, D.D. (1997) Respondent-Driven Sampling: A New Approach to the Study of Hidden Populations. Social Problems, 44, 174-199. http://dx.doi.org/10.2307/3096941

has been cited by the following article:

  • TITLE: Estimating Vertex Measures in Social Networks by Sampling Completions of RDS Trees

    AUTHORS: Bilal Khan, Kirk Dombrowski, Ric Curtis, Travis Wendel

    KEYWORDS: Network Imputation, Missing Data, Spanning Tree Completions, Respondent-Driven Sampling

    JOURNAL NAME: Social Networking, Vol.4 No.1, January 13, 2015

    ABSTRACT: This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of “estimating connectivity from spanning tree completions” (ECSTC) is specifically designed to address situations where only spanning tree(s) of a network are known, such as those obtained through respondent driven sampling (RDS). Using repeated random completions derived from degree information, this method forgoes the usual step of trying to obtain final edge or vertex rosters, and instead aims to estimate network-centric properties of vertices probabilistically from the spanning trees themselves. In this paper, we discuss the problem of missing data and describe the protocols of our completion method, and finally the results of an experiment where ECSTC was used to estimate graph dependent vertex properties from spanning trees sampled from a graph whose characteristics were known ahead of time. The results show that ECSTC methods hold more promise for obtaining network-centric properties of individuals from a limited set of data than researchers may have previously assumed. Such an approach represents a break with past strategies of working with missing data which have mainly sought means to complete the graph, rather than ECSTC’s approach, which is to estimate network properties themselves without deciding on the final edge set.