CAb-NC: The correspondence analysis based network clustering method

Masaomi Kimura

doi:10.1145/3341161.3342944

CAb-NC: The correspondence analysis based network clustering method

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Finding clusters in a network has been practically important in many applications and was studied by many researchers. Most commonly used methods are spectral clustering and Newman’s modularity maximization. However, there has been no unified view of them. In this study, we introduced a new guiding principle based on correspondence analysis to obtain nodes’ coordinates and discussed its equivalence to spectral clustering and its relationship to Newman’s modularity.

Original language	English
Title of host publication	Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
Editors	Francesca Spezzano, Wei Chen, Xiaokui Xiao
Publisher	Association for Computing Machinery, Inc
Pages	538-539
Number of pages	2
ISBN (Electronic)	9781450368681
DOIs	https://doi.org/10.1145/3341161.3342944
Publication status	Published - 2019 Aug 27
Event	11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 - Vancouver, Canada Duration: 2019 Aug 27 → 2019 Aug 30

Publication series

Name	Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

Conference

Conference	11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
Country	Canada
City	Vancouver
Period	19/8/27 → 19/8/30

Fingerprint

ASJC Scopus subject areas

Communication
Computer Networks and Communications
Information Systems and Management
Sociology and Political Science

Cite this

Kimura, M. (2019). CAb-NC: The correspondence analysis based network clustering method. In F. Spezzano, W. Chen, & X. Xiao (Eds.), Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 (pp. 538-539). (Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3341161.3342944

CAb-NC : The correspondence analysis based network clustering method. / Kimura, Masaomi.

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019. ed. / Francesca Spezzano; Wei Chen; Xiaokui Xiao. Association for Computing Machinery, Inc, 2019. p. 538-539 (Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kimura, M 2019, CAb-NC: The correspondence analysis based network clustering method. in F Spezzano, W Chen & X Xiao (eds), Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019. Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019, Association for Computing Machinery, Inc, pp. 538-539, 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019, Vancouver, Canada, 19/8/27. https://doi.org/10.1145/3341161.3342944

Kimura M. CAb-NC: The correspondence analysis based network clustering method. In Spezzano F, Chen W, Xiao X, editors, Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019. Association for Computing Machinery, Inc. 2019. p. 538-539. (Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019). https://doi.org/10.1145/3341161.3342944

Kimura, Masaomi. / CAb-NC : The correspondence analysis based network clustering method. Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019. editor / Francesca Spezzano ; Wei Chen ; Xiaokui Xiao. Association for Computing Machinery, Inc, 2019. pp. 538-539 (Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019).

@inproceedings{e9fcb68dec8548fd9f5189f0ee2cb35b,

title = "CAb-NC: The correspondence analysis based network clustering method",

abstract = "Finding clusters in a network has been practically important in many applications and was studied by many researchers. Most commonly used methods are spectral clustering and Newman{\textquoteright}s modularity maximization. However, there has been no unified view of them. In this study, we introduced a new guiding principle based on correspondence analysis to obtain nodes{\textquoteright} coordinates and discussed its equivalence to spectral clustering and its relationship to Newman{\textquoteright}s modularity.",

author = "Masaomi Kimura",

year = "2019",

month = aug

day = "27",

doi = "10.1145/3341161.3342944",

language = "English",

series = "Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019",

publisher = "Association for Computing Machinery, Inc",

pages = "538--539",

editor = "Francesca Spezzano and Wei Chen and Xiaokui Xiao",

booktitle = "Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019",

note = "11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 ; Conference date: 27-08-2019 Through 30-08-2019",

}

TY - GEN

T1 - CAb-NC

T2 - 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

AU - Kimura, Masaomi

PY - 2019/8/27

Y1 - 2019/8/27

N2 - Finding clusters in a network has been practically important in many applications and was studied by many researchers. Most commonly used methods are spectral clustering and Newman’s modularity maximization. However, there has been no unified view of them. In this study, we introduced a new guiding principle based on correspondence analysis to obtain nodes’ coordinates and discussed its equivalence to spectral clustering and its relationship to Newman’s modularity.

AB - Finding clusters in a network has been practically important in many applications and was studied by many researchers. Most commonly used methods are spectral clustering and Newman’s modularity maximization. However, there has been no unified view of them. In this study, we introduced a new guiding principle based on correspondence analysis to obtain nodes’ coordinates and discussed its equivalence to spectral clustering and its relationship to Newman’s modularity.

UR - http://www.scopus.com/inward/record.url?scp=85078868398&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85078868398&partnerID=8YFLogxK

U2 - 10.1145/3341161.3342944

DO - 10.1145/3341161.3342944

M3 - Conference contribution

AN - SCOPUS:85078868398

T3 - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

SP - 538

EP - 539

BT - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

A2 - Spezzano, Francesca

A2 - Chen, Wei

A2 - Xiao, Xiaokui

PB - Association for Computing Machinery, Inc

Y2 - 27 August 2019 through 30 August 2019

ER -

Access to Document

10.1145/3341161.3342944