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Heavy Tail Phenomena in in Preferential Attachment Networks
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Heavy Tail Phenomena in in Preferential Attachment Networks. |
| 개인저자 | Wang, Tiandong. |
| 단체저자명 | Cornell University. Operations Research and Information Engineering. |
| 발행사항 | [S.l.]: Cornell University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 152 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781085792301 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Resnick, Sidney. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Preferential attachment is widely used to model the power-law behavior of degree distributions in social networks. In this thesis, we study three aspects of a directed preferential attachment model. First, we consider fitting this network model under different data scenarios. We propose both parametric and semi-parametric estimation procedures and compare the corresponding estimating results. Second, we see from empirical studies that statistical estimates of the marginal tail exponent of the power-law degree distribution often use the Hill estimator, even though no theoretical justification has been given. Hence, we study the convergence of the joint empirical measure for in- and out-degrees and prove the consistency of the Hill estimator for the preferential attachment model. Finally, we consider a widely adopted threshold selection procedure when estimating the power-law index in practice and examine the asymptotic behavior of the selected threshold as well as the corresponding power-law index given. |
| 일반주제명 | Operations research. Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
