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AMiner: Search and Mining of Academic Social Networks

文献类型:期刊论文

作者Huaiyu Wan3; Yutao Zhang2; Jing Zhang1; Jie Tang2
刊名Data Intelligence
出版日期2019-03-25
卷号1期号:1页码:58-76
关键词Academic Social Networks Profile Extraction Name Disambiguation Topic Modeling Expertise Search Network Mining
DOI10.1162/dint_a_00006
英文摘要

AMiner is a novel online academic search and mining system, and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous
networks formed by authors, papers, conferences, journals and organizations. The system is subsequently able to extract researchers’ profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation. Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search. In
addition, AMiner offers a set of researcher-centered functions, including social influence analysis, relationship mining, collaboration recommendation, similarity analysis and community evolution. The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions.

URL标识查看原文
语种英语
源URL[http://ir.las.ac.cn/handle/12502/11459]  
专题中国科学院文献情报中心(北京)_编辑出版中心_《数据智能》(英文)
通讯作者Jie Tang
作者单位1.Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
2.Information School, Renmin University of China, Beijing 100872, China
3.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
推荐引用方式
GB/T 7714
Huaiyu Wan,Yutao Zhang,Jing Zhang,et al. AMiner: Search and Mining of Academic Social Networks[J]. Data Intelligence,2019,1(1):58-76.
APA Huaiyu Wan,Yutao Zhang,Jing Zhang,&Jie Tang.(2019).AMiner: Search and Mining of Academic Social Networks.Data Intelligence,1(1),58-76.
MLA Huaiyu Wan,et al."AMiner: Search and Mining of Academic Social Networks".Data Intelligence 1.1(2019):58-76.

入库方式: OAI收割

来源:文献情报中心

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