Regression Analysis of Asymmetric Pairs in Large-Scale Network Data

12 Pages Posted: 10 Oct 2011

See all articles by Rui Pan

Rui Pan

Peking University

Hansheng Wang

Peking University - Guanghua School of Management

Chih-Ling Tsai

University of California, Davis - Graduate School of Management

Date Written: October 9, 2011

Abstract

In social network analysis, many estimation methods have been developed over the past three decades. Due to the computational complexity for analyzing large-scale social network data, however, those methods cannot be applied effectively. On the other hand, the structure of large-scale network data is often sparse so that the information loss by ignoring symmetric pairs is rather limited. Hence, we propose an asymmetric pairs regression (APR) approach to study the social network relationship. Accordingly, the computation of parameter estimations is simple and the theoretical properties can be obtained via the established logistic regression model. Simulation studies and an empirical example are presented to illustrate the usefulness of APR.

Keywords: Asymmetric Pair, Dyad Independence Model, Latent Space Model, Social Network, Stochastic Block Model

JEL Classification: C10, C20

Suggested Citation

Pan, Rui and Wang, Hansheng and Tsai, Chih-Ling, Regression Analysis of Asymmetric Pairs in Large-Scale Network Data (October 9, 2011). Available at SSRN: https://ssrn.com/abstract=1941227 or http://dx.doi.org/10.2139/ssrn.1941227

Rui Pan

Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

Hansheng Wang (Contact Author)

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

HOME PAGE: http://hansheng.gsm.pku.edu.cn

Chih-Ling Tsai

University of California, Davis - Graduate School of Management ( email )

One Shields Avenue
Davis, CA 95616
United States

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