Electronic Journal of Statistics

Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding

Vince Lyzinski, Daniel L. Sussman, Minh Tang, Avanti Athreya, and Carey E. Priebe

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Abstract

Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research. In this paper, we provide a short proof that the adjacency spectral embedding can be used to obtain perfect clustering for the stochastic blockmodel and the degree-corrected stochastic blockmodel. We also show an analogous result for the more general random dot product graph model.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 2905-2922.

Dates
First available in Project Euclid: 9 January 2015

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1420815881

Digital Object Identifier
doi:10.1214/14-EJS978

Mathematical Reviews number (MathSciNet)
MR3299126

Zentralblatt MATH identifier
1308.62131

Subjects
Primary: 62C30
Secondary: 05C80: Random graphs [See also 60B20]

Keywords
Clustering stochastic block model degree corrected stochastic block model

Citation

Lyzinski, Vince; Sussman, Daniel L.; Tang, Minh; Athreya, Avanti; Priebe, Carey E. Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding. Electron. J. Statist. 8 (2014), no. 2, 2905--2922. doi:10.1214/14-EJS978. https://projecteuclid.org/euclid.ejs/1420815881


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References

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