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Semidefinite Programming Approaches to Network Clustering and Smoothing
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Yan, Zhifei. |
| 단체저자명 | The Ohio State University. Statistics. |
| 서명/저자사항 | Semidefinite Programming Approaches to Network Clustering and Smoothing. |
| 발행사항 | [S.l.] : The Ohio State University., 2017 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2017 |
| 형태사항 | 111 p. |
| 소장본 주기 | School code: 0168. |
| ISBN | 9780438097353 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Vincent Vu. |
| 요약 | Community detection and link prediction are two important problems in network analysis. In this dissertation, we propose semidefinite programming approaches to network community detection and edge probabilities estimation. Interestingly, despite |
| 요약 | For community detection, the SDP is derived from the partition criterion of maximizing the sum of average intra-cluster similarities over all clusters. The feasible set of our SDP is contained in the Fantope, which enables us to connect our SDP |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International79-10B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T15000269 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00024969 | 310 | 가야대학교// | 대출가능 |
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