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Activity Number: 524 - Emerging Statistical Learning Methods in Modern Data Science
Type: Invited
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #309440
Title: A Community Model for Partially Observed Networks from Surveys
Author(s): Tianxi Li* and Elizaveta Levina and Ji Zhu
Companies: University of Virginia and University of Michigan and University of Michigan
Keywords: Community detection; networks; partial observation; survey

Communities are an important type of structure in networks and they have been widely studied. In practice, network data are often collected through sampling mechanisms, such as survey questionnaires, instead of direct observation. The noise and bias introduced by such sampling mechanisms can obscure the community structure and invalidate the assumptions of standard community detection methods. We propose a model to incorporate neighborhood sampling, through a model reflective of survey designs, into community detection for directed networks, since friendship networks obtained from surveys are naturally directed. We model the edge sampling probabilities as a function of both individual preferences and community parameters, and fit the model by a combination of spectral clustering and the method of moments. The algorithm is computationally efficient and comes with a theoretical guarantee of consistency. We evaluate the proposed model in extensive simulation studies and applied it to a faculty hiring dataset, discovering a meaningful hierarchy of communities among US business schools.

Authors who are presenting talks have a * after their name.

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