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Spectral analysis of rich network to...
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Wu, Leting.
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Spectral analysis of rich network topology in social networks.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Spectral analysis of rich network topology in social networks./
作者:
Wu, Leting.
面頁冊數:
164 p.
附註:
Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:
Dissertation Abstracts International74-09B(E).
標題:
Sociology, Theory and Methods. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3563276
ISBN:
9781303114939
Spectral analysis of rich network topology in social networks.
Wu, Leting.
Spectral analysis of rich network topology in social networks.
- 164 p.
Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2013.
Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few theoretical and systematical studies to support their findings.
ISBN: 9781303114939Subjects--Topical Terms:
626625
Sociology, Theory and Methods.
Spectral analysis of rich network topology in social networks.
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Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few theoretical and systematical studies to support their findings.
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In this dissertation, we conduct an in-depth theoretical study to show the close relationship between algebraic spectral properties of the adjacency matrix and various patterns in a broad range of social networks such as friendship networks, alliance and war networks, and distrusted networks. In our framework, we apply matrix perturbation theory and approxima.
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