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AMiner: Search and Mining of Academic Social Networks

Authors :
Wan, Huaiyu
Zhang, Yutao
Zhang, Jing
Tang, Jie
Source :
Data Intelligence, Vol 1, Iss 1, Pp 58-76 (2019)
Publication Year :
2019
Publisher :
The MIT Press, 2019.

Abstract

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.

Subjects

Subjects :
Information technology
T58.5-58.64

Details

Language :
English
ISSN :
2641435X
Volume :
1
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Data Intelligence
Publication Type :
Academic Journal
Accession number :
edsdoj.325256431cc84608847470a21b3bf02f
Document Type :
article
Full Text :
https://doi.org/10.1162/dint_a_00006