Detecting Latent Communities in Network Formation Models

Shujie Ma (), Liangjun Su () and Yichong Zhang ()
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Shujie Ma: University of California, Riverside
Yichong Zhang: School of Economics, Singapore Management University

No 12-2020, Economics and Statistics Working Papers from Singapore Management University, School of Economics

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 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2020-05-07
New Economics Papers: this item is included in nep-net and nep-sea
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Citations: View citations in EconPapers (7)

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Working Paper: Detecting Latent Communities in Network Formation Models (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2020_012

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