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Learning with Attributed Networks: Algorithms and Applications
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Learning with Attributed Networks: Algorithms and Applications. |
| 개인저자 | Li, Jundong. |
| 단체저자명 | Arizona State University. Computer Science. |
| 발행사항 | [S.l.]: Arizona State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 163 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085687324 |
| 학위논문주기 | Thesis (Ph.D.)--Arizona State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Liu, Huan. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast amounts of data that can be structured into attributed networks, and their use has been attractive to researchers and practitioners in different disciplines. For example, in social media, users interact with each other and also post personalized content |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
