Presentation 2022-03-10
Optimum Clustering Method for Data Driven Consensus Problem considering Network Centrality
Shoya Ogawa, Koji Ishii,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In consensus problems in complex networks, the convergence performance deeply depends on the weighting factors. IKishida et.al. have proposed the data-driven optimization method for the consensus problem with a complex network and that optimized weights can significantly improve the convergence performance. However, since it provides the optimum weighting factors only for the focused network topology, the calculated weighting factors cannot be applied to the case with different network topology. The authors have previously proposed the data-driven optimization method with the constraint that the nodes with the same network centrality should have the same weighting factor, which leads to the versatility of the applied network topology. Although there are several types of centrality, in this study, degree centrality, eigenvector centrality, betweenness centrality, and PageRank are used as constraints. Since centrality other than degree centrality is given as a continuous value, it is necessary to cluster the continuous values in order to use them as constraints. The clustering method is meant to map the centrality to the constraints, thus its design criterion determines the convergence performance. This study reveals the relationship between the clustering methods and the performance, and provides a suboptimal clustering methods corresponding to the type of network topology as well as the type of applied network centrality.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Consensus Problem / data-driven algorithm / deep-unfolding / network centrality
Paper # IT2021-109,ISEC2021-74,WBS2021-77,RCC2021-84
Date of Issue 2022-03-03 (IT, ISEC, WBS, RCC)

Conference Information
Committee IT / ISEC / RCC / WBS
Conference Date 2022/3/10(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Joint Meeting of ISEC, IT, RCC, and WBS
Chair Tadashi Wadayama(Nagoya Inst. of Tech.) / Tetsuya Izu(Fujitsu Labs.) / HUAN-BANG LI(NICT) / Masanori Hamamura(Kochi Univ. of Tech.)
Vice Chair Tetsuya Kojima(Tokyo Kosen) / Noboru Kunihiro(Tsukuba Univ.) / Goichiro Hanaoka(AIST) / Shunichi Azuma(Nagoya Univ.) / Koji Ishii(Kagawa Univ.) / Takashi Shono(INTEL) / Masahiro Fujii(Utsunomiya Univ.)
Secretary Tetsuya Kojima(Saitamai Univ.) / Noboru Kunihiro(Yamaguchi Univ.) / Goichiro Hanaoka(Fujitsu Labs.) / Shunichi Azuma(Ibaraki Univ.) / Koji Ishii(CRIEPI) / Takashi Shono(Osaka Univ.) / Masahiro Fujii(National Defence Academy)
Assistant Masanori Hirotomo(Saga Univ.) / Takahiro Matsuda(AIST) / SHAN LIN(NICT) / Masaki Ogura(Osaka Univ.) / Masayuki Kinoshita(Chiba Univ. of Tech.) / Sun Ran(Ibaraki Univ.)

Paper Information
Registration To Technical Committee on Information Theory / Technical Committee on Information Security / Technical Committee on Reliable Communication and Control / Technical Committee on Wideband System
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Optimum Clustering Method for Data Driven Consensus Problem considering Network Centrality
Sub Title (in English)
Keyword(1) Consensus Problem
Keyword(2) data-driven algorithm
Keyword(3) deep-unfolding
Keyword(4) network centrality
1st Author's Name Shoya Ogawa
1st Author's Affiliation Kagawa University(Kagawa Univ)
2nd Author's Name Koji Ishii
2nd Author's Affiliation Kagawa University(Kagawa Univ)
Date 2022-03-10
Paper # IT2021-109,ISEC2021-74,WBS2021-77,RCC2021-84
Volume (vol) vol.121
Number (no) IT-428,ISEC-429,WBS-430,RCC-431
Page pp.pp.155-160(IT), pp.155-160(ISEC), pp.155-160(WBS), pp.155-160(RCC),
#Pages 6
Date of Issue 2022-03-03 (IT, ISEC, WBS, RCC)