Local Graph Clustering with Network Lasso

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021
Major/Subject
Mcode
Degree programme
Language
en
Pages
5
106-110
Series
IEEE Signal Processing Letters, Volume 28
Abstract
We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundaries and seed nodes. While spectral clustering methods are guided by a minimization of the graph Laplacian quadratic form, nLasso minimizes the total variation of cluster indicator signals. As demonstrated theoretically and numerically, nLasso methods can handle very sparse clusters (chain-like) which are difficult for spectral clustering. We also verify that a primal-dual method for non-smooth optimization allows to approximate nLasso solutions with optimal worst-case convergence rate.
Description
Keywords
Clustering methods, Convergence, Laplace equations, Message passing, Minimization, Optimization, TV
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Citation
Jung , A & Sarcheshmehpour , Y 2021 , ' Local Graph Clustering with Network Lasso ' , IEEE Signal Processing Letters , vol. 28 , 9298875 , pp. 106-110 . https://doi.org/10.1109/LSP.2020.3045832