Scientific ranking over heterogeneous academic hypernetwork

Ronghua Liang, Xiaorui Jiang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

Ranking is an important way of retrieving authoritative papers from a large scientific literature database. Current state- of-The-Art exploits the flat structure of the heterogeneous academic network to achieve a better ranking of scientific articles, however, ignores the multinomial nature of the multidimensional relationships between different types of academic entities. This paper proposes a novel mutual ranking algorithm based on the multinomial heterogeneous academic hypernetwork, which serves as a generalized model of a scientific literature database. The proposed algorithm is demonstrated effective through extensive evaluation against well-known IR metrics on a well-established benchmarking environment based on the ACL Anthology Network.

Original languageEnglish
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI
Pages20-26
Number of pages7
ISBN (Electronic)978-1-5773-5760-5
Publication statusPublished - 17 Feb 2016
Event30th AAAI Conference on Artificial Intelligence - Phoenix, AZ, United States
Duration: 12 Feb 201617 Feb 2016

Conference

Conference30th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2016
CountryUnited States
CityPhoenix, AZ
Period12/02/1617/02/16

Bibliographical note

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    Liang, R., & Jiang, X. (2016). Scientific ranking over heterogeneous academic hypernetwork. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 20-26). AAAI.