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research article

Clustering with Multi-Layer Graphs: A Spectral Perspective

Dong, Xiaowen  
•
Frossard, Pascal  
•
Vandergheynst, Pierre  
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2012
IEEE Transactions on Signal Processing

Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (users) with different edges (pairwise relationships). In this paper, we address the problem of combining different layers of the multi-layer graph for improved clustering of the vertices compared to using layers independently. We propose two novel methods, which are based on joint matrix factorization and graph regularization framework respectively, to efficiently combine the spectrum of the multiple graph layers, namely the eigenvectors of the graph Laplacian matrices. In each case, the resulting combination, which we call a “joint spectrum” of multiple graphs, is used for clustering the vertices. We evaluate our approaches by simulations with several real world social network datasets. Results demonstrate the superior or competitive performance of the proposed methods over state-of-the-art technique and common baseline methods, such as co-regularization and summation of information from individual graphs.

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Type
research article
DOI
10.1109/Tsp.2012.2212886
Web of Science ID

WOS:000310139900017

ArXiv ID

1106.2233

Author(s)
Dong, Xiaowen  
Frossard, Pascal  
Vandergheynst, Pierre  
Nefedov, Nikolai
Date Issued

2012

Publisher

Institute of Electrical and Electronics Engineers

Published in
IEEE Transactions on Signal Processing
Volume

60

Issue

11

Start page

5820

End page

5831

Subjects

Multi-layer graph

•

spectrum of the graph

•

matrix factorization

•

graph-based regularization

•

clustering

•

LTS4

•

LTS2

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LTS4  
LTS2  
Available on Infoscience
June 10, 2011
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/68605
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