Shujie Ma, Liangjun Su, Yichong Zhang
arXiv 7 May 2020 · Econometrics · 7 citations (OpenAlex)
arXiv:2005.03226 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, V., C. Hansen, Y. Liao, and Y. Zhu (2020) Inference for heterogeneous effects using low-rank estimations | 0.976 | 14 | 3 | 93% |
| 2 | Graham, B. S (2017) An econometric model of network formation with degree heterogeneity | 0.874 | 12 | 4 | 67% |
| 3 | Su, L., W. Wang, and Y. Zhang (2020) Strong consistency of spectral clustering for stochastic block models self | 0.843 | 5 | 4 | 60% |
| 4 | Roy, S., Y. Atchade, and G. Michailidis (2019) Likelihood inference for large scale stochastic blockmodels with covariates based on a divide-and-conquer parallelizable algorit… | 0.811 | 4 | 2 | 100% |
| 5 | Vu, V (2018) A simple svd algorithm for finding hidden partitions | 0.737 | 3 | 2 | 100% |
| 6 | Moon, H. R. and M. Weidner (2018) Nuclear norm regularized estimation of panel regression models | 0.644 | 4 | 1 | 100% |
| 7 | Abbe, E., A. S. Bandeira, and G. Hall (2016) Exact recovery in the stochastic block model | 0.644 | 2 | 2 | 100% |
| 8 | Abbe, E. and C. Sandon (2015) Community detection in general stochastic block models: Fundamental limits and efficient algorithms for recovery | 0.644 | 2 | 2 | 100% |
| 9 | Holland, P. W., K. B. Laskey, and S. Leinhardt (1983) Stochastic blockmodels: First steps | 0.644 | 2 | 2 | 100% |
| 10 | Mossel, E., J. Neeman, and A. Sly (2014) Consistency thresholds for binary symmetric block models | 0.644 | 2 | 2 | 100% |
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