TY - JOUR
T1 - Correspondence analysis-based network clustering and importance of degenerate solutions unification of spectral clustering and modularity maximization
AU - Kimura, Masaomi
N1 - Publisher Copyright:
© 2020, Springer-Verlag GmbH Austria, part of Springer Nature.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Methods to find clusters in a network have been studied extensively because clustering has practical importance in many applications. Commonly used methods include spectral clustering and Newman’s modularity maximization. However, there has been no unified view of the two methods. In this study, we introduce an innovative guiding principle based on correspondence analysis to obtain node coordinates and discuss its equivalence to spectral clustering and Newman’s modularity. Besides, we discuss a degeneration case and its significance.
AB - Methods to find clusters in a network have been studied extensively because clustering has practical importance in many applications. Commonly used methods include spectral clustering and Newman’s modularity maximization. However, there has been no unified view of the two methods. In this study, we introduce an innovative guiding principle based on correspondence analysis to obtain node coordinates and discuss its equivalence to spectral clustering and Newman’s modularity. Besides, we discuss a degeneration case and its significance.
KW - Correspondence analysis
KW - Modularity maximization
KW - Network clustering
KW - Spectral clustering
UR - http://www.scopus.com/inward/record.url?scp=85089771058&partnerID=8YFLogxK
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U2 - 10.1007/s13278-020-00686-z
DO - 10.1007/s13278-020-00686-z
M3 - Article
AN - SCOPUS:85089771058
SN - 1869-5450
VL - 10
JO - Social Network Analysis and Mining
JF - Social Network Analysis and Mining
IS - 1
M1 - 71
ER -