New approach to sub-community structure in high-dimensional data = 고차원 데이터의 서브 커뮤니티에 대한 새로운 접근

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For complex systems described as networks, modularity maximization has been emerging as one of community detection methods like PCA and network analysis, due to their intuitive concept and application potential to real systems in spite of the resolution limit. By the traditional methods, however, sub-community structure may not be clearly revealed in many cases. For the complex system of which nodes are expressed in high-dimensional feature vectors, we propose a new procedure using archetypal analysis (AA) and an invented quality function for uncovering the multiscale community structure of the system, visualize the structure with t-SNE and also use machine learning techniques for optimization issues. In this thesis, we study macro- and sub-community structures of various complex systems including generated system, financial system and bioinformatics system with the proposed approach and other traditional methods, and also show that the proposed one can overcome the limit of traditional ones in community detection.
Advisors
Kim, Sooyongresearcher김수용researcher
Description
한국과학기술원 :물리학과,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 물리학과, 2017.2,[vii, 74 p. :]

Keywords

community; network; modularity; quality funciton; archetypal analysis; t-SNE; PCA; 커뮤니티; 네트워크; 모듈러리티; 품질함수; 원형분석

URI
http://hdl.handle.net/10203/241765
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675692&flag=dissertation
Appears in Collection
PH-Theses_Ph.D.(박사논문)
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