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Title:
pav: a novel model for ranking heterogeneous objects in bibliographic information networks
Author: Deng Zhi-Hong ; Lai Bo-Yan ; Wang Zhong-Hui ; Fang Guo-Dong
Source: Expert Systems with Applications
Issued Date: 2012
Volume: 39, Issue:10, Pages:9788-9796
Indexed Type: ei
Department: (1) Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China; (2) State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
English Abstract: Bibliographic information networks, formed by online bibliographic databases, such as ACM Digital Library and IEEE/IET Electronic Library, contain abundant information about authors, papers, venues (journals/conferences), and have been widely studies in recent years. However, few studies examine the problem of ranking objects in these networks. In this paper, we study this problem and present a novel model, called PAV, for ranking heterogeneous objects, such as authors, papers, and venues. Based on PAV model, we transform the problem of ranking objects into the problem of estimating probability distribution. We propose an efficient algorithm to estimate probability parameters by use of the fact that the PAV model is a regular Markov chain. For evaluating PAV model, we apply it on one real dataset, which was crawled from ACM Digital Library. The experimental results show that the proposed model is effective. © 2012 Elsevier Ltd. All rights reserved.
Language: 英语
WOS ID: WOS:000303281800131
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/14722
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Deng Zhi-Hong,Lai Bo-Yan,Wang Zhong-Hui,et al. pav: a novel model for ranking heterogeneous objects in bibliographic information networks[J]. Expert Systems with Applications,2012-01-01,39(10):9788-9796.
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