Publication Type
Working Paper
Version
publishedVersion
Publication Date
5-2020
Abstract
This paper proposes a logistic undirected network formation model which allows for assortative matching on observed individual characteristics and the presence of edge-wise fixed effects. We model the coefficients of observed characteristics to have a latent community structure and the edge-wise fixed effects to be of low rank. We propose a multi-step estimation procedure involving nuclear norm regularization, sample splitting, iterative logistic regression and spectral clustering to detect the latent communities. We show that the latent communities can be exactly recovered when the expected degree of the network is of order log n or higher, where n is the number of nodes in the network. The finite sample performance of the new estimation and inference methods is illustrated through both simulated and real datasets.
Keywords
Community detection, homophily, spectral clustering, strong consistency, unobserved heterogeneity
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
55
Publisher
SMU Economics and Statistics Working Paper Series, Paper No. 12-2020
City or Country
Singapore
Citation
MA, Shujie; SU, Liangjun; and ZHANG, Yichong.
Detecting latent communities in network formation models. (2020). 1-55.
Available at: https://ink.library.smu.edu.sg/soe_research/2377
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.