Data Collection and Estimation in Networks Through Cognitive Social Structures

• Activity Number: 30
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Deniz Yenigun*+ and Gunes Ertan and Michael Siciliano
• Companies: Bilkent University and University of Pittsburgh and University of Pittsburgh
• Keywords: social networks ; network sampling ; cognitive social structures

Abstract

We introduce a novel sampling and estimation methodology for identifying unknown networks or network measures of interest. Rather than collecting data to observe a network full, which is typically costly or impossible, we randomly sample a portion of individuals in the network and estimate the network based on the sampled individuals' perceptions on all possible ties. We find the methodology produces accurate estimates of social structure and network level indices in five different datasets. In order to illustrate the performance of our approach we compare its results with the traditional roster and ego network methods of data collection. Across all five datasets, our methodology outperforms these standard social network data collection methods. We offer ideas on applications of our methodology in cross-network settings. The methods introduced are applicable to research fields in organization theory, public administration, health policy, education policy, and other areas of research concerning studies of human associations.