Spectral methods for graph clustering – A survey

Graph clustering is an area in cluster analysis that looks for groups of related vertices in a graph. Due to its large applicability, several graph clustering algorithms have been proposed in the last years. A particular class of graph clustering algorithms is known as spectral clustering algorithms...

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Bibliographic Details
Published in:European journal of operational research Vol. 211; no. 2; pp. 221 - 231
Main Authors: Nascimento, Mariá C.V., de Carvalho, André C.P.L.F.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.06.2011
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Cluster analysis
Clustering
Computer science; control theory; systems
Cutting stock problem
Data processing. List processing. Character string processing
Exact sciences and technology
Graph algorithms
Graphs
Information retrieval. Graph
Laplace transforms
Mathematical analysis
Matrix
Memory organisation. Data processing
Min-cut
Modularity
ncut
Operational research
Partitioning
Ratio cut
Software
Spectra
Spectral clustering
Spectral clustering Min-cut Ratio cut ncut Modularity
Spectral methods
Studies
Theoretical computing
Spectral clustering
Ratio cut
Modularity
Min-cut
ncut
Cluster analysis
Spectral method
Modular system
Graph theory
Data mining
Weighted graph
Laplacian
Graph decomposition
Graph cut
Classification
Graph method
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:Graph clustering is an area in cluster analysis that looks for groups of related vertices in a graph. Due to its large applicability, several graph clustering algorithms have been proposed in the last years. A particular class of graph clustering algorithms is known as spectral clustering algorithms. These algorithms are mostly based on the eigen-decomposition of Laplacian matrices of either weighted or unweighted graphs. This survey presents different graph clustering formulations, most of which based on graph cut and partitioning problems, and describes the main spectral clustering algorithms found in literature that solve these problems.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2010.08.012