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AMiner: Search and Mining of Academic Social Networks
Author(s) -
Huaiyu Wan,
Yutao Zhang,
Jing Zhang,
Jie Tang
Publication year - 2019
Publication title -
data intelligence
Language(s) - English
Resource type - Journals
eISSN - 2096-7004
pISSN - 2641-435X
DOI - 10.1162/dint_a_00006
Subject(s) - computer science , process (computing) , set (abstract data type) , data science , similarity (geometry) , social network analysis , world wide web , probabilistic logic , generative model , information retrieval , generative grammar , social media , artificial intelligence , image (mathematics) , programming language , operating system
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.

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