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