Exploiting Heterogeneous Scientific Literature Networks to Combat Ranking Bias: Evidence From the Computational Linguistics Area

doi:10.1002/asi.23463

	Exploiting Heterogeneous Scientific Literature Networks to Combat Ranking Bias: Evidence From the Computational Linguistics Area
	Jiang, Xiaorui 1; Sun, Xiaoping 2; Yang, Zhe 3; Hai Zhuge 2,4; Yao, Jianmin 5
	2016-07-01
发表期刊	JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
ISSN	2330-1635
卷号	67 期号:7 页码:1679-1702
摘要	It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.
关键词	bibliometrics
DOI	10.1002/asi.23463
收录类别	SCI
语种	英语
资助项目	National Science Foundation of Zhejiang Province[LY14F020016] ; National Science Foundation of Zhejiang Province[LR14F020002] ; National Science Foundation of China[1402412] ; National Science Foundation of China[K111818612]
WOS研究方向	Computer Science ; Information Science & Library Science
WOS类目	Computer Science, Information Systems ; Information Science & Library Science
WOS记录号	WOS:000378644700010
出版者	WILEY-BLACKWELL
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8321
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jiang, Xiaorui
作者单位	1.Zhejiang Univ Technol, Sch Informat Engn, 288 Liuhe Rd, Hangzhou 310023, Zhejiang, Peoples R China 2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China 3.Zhejiang Univ, Dept Informat Sci & Elect Engn, 37 Zheda Rd, Hangzhou 310007, Zhejiang, Peoples R China 4.Aston Univ, Birmingham B4 7ET, W Midlands, England 5.Soochow Univ, Sch Comp Sci & Technol, 1 Shizi Rd, Suzhou 215006, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Xiaorui,Sun, Xiaoping,Yang, Zhe,et al. Exploiting Heterogeneous Scientific Literature Networks to Combat Ranking Bias: Evidence From the Computational Linguistics Area[J]. JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY,2016,67(7):1679-1702.
APA	Jiang, Xiaorui,Sun, Xiaoping,Yang, Zhe,Hai Zhuge,&Yao, Jianmin.(2016).Exploiting Heterogeneous Scientific Literature Networks to Combat Ranking Bias: Evidence From the Computational Linguistics Area.JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY,67(7),1679-1702.
MLA	Jiang, Xiaorui,et al."Exploiting Heterogeneous Scientific Literature Networks to Combat Ranking Bias: Evidence From the Computational Linguistics Area".JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY 67.7(2016):1679-1702.