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Activity Number: 433
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320697 View Presentation
Title: Lasso-Type Network Community Detection Within Latent Space
Author(s): Shiwen Shen* and Edsel Aldea Pena
Companies: and University of South Carolina
Keywords: Network Analysis ; Community Detection ; Latent Space Models ; Lasso ; ADMM

Community detection is one of the fundamental topics in the statistical network analysis. Many methods have been proposed in the literature to solve this problem, however, approaches are not feasible for large size network data. In this paper, we propose a model to shrink the distances among nodes inside a network using the lasso penalty, after projecting nodes, edges as well as related covariates into a prespecified latent space. We use the alternating direction method of multipliers (ADMM) algorithm in the estimation procedure to make the model be friendly to large size data. Simulation results and an example using open sourced data are provided in detail.

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

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