Imperial College London
Portrait Picture

Professor Nick Heard

Faculty of Natural Sciences, Department of Mathematics

Chair in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 1490n.heard Website

 
 
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Location

 

543Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sanna:2022:10.1080/00401706.2021.2008503,
author = {Sanna, Passino F and Heard, NA and Rubin-Delanchy, P},
doi = {10.1080/00401706.2021.2008503},
journal = {Technometrics},
pages = {346--357},
title = {Spectral clustering on spherical coordinates under the degree-corrected  stochastic blockmodel},
url = {http://dx.doi.org/10.1080/00401706.2021.2008503},
volume = {64},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Spectral clustering is a popular method for community detection in networksunder the assumption of the standard stochastic blockmodel. Taking a matrix representation of the graph such as the adjacency matrix, the nodes are clustered on a low dimensional projection obtained from a truncated spectral decomposition of the matrix. Estimating the number of communities and the dimension of the reduced latent space well is crucial for good performance of spectral clustering algorithms. Real-world networks, such as computer networks studied in cyber-security applications, often present heterogeneous within-community degree distributions which are better addressed by the degree-corrected stochastic blockmodel. A novel, model-based method is proposed in this article for simultaneous and automated selection of the number of communities and latent dimension for spectral clustering under the degree-corrected stochastic blockmodel. The method is based on a transformation to spherical coordinates of the spectral embedding, and on a novel modelling assumption in the transformed space, which is then embedded into an existing model selection framework for estimating the number of communities and the latent dimension. Results show improved performance over competing methods on simulated and real-world computer network data.
AU  - Sanna,Passino F
AU  - Heard,NA
AU  - Rubin-Delanchy,P
DO  - 10.1080/00401706.2021.2008503
EP  - 357
PY  - 2022///
SN  - 0040-1706
SP  - 346
TI  - Spectral clustering on spherical coordinates under the degree-corrected  stochastic blockmodel
T2  - Technometrics
UR  - http://dx.doi.org/10.1080/00401706.2021.2008503
UR  - http://arxiv.org/abs/2011.04558v1
UR  - http://hdl.handle.net/10044/1/89017
VL  - 64
ER  -
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