Scientific Ranking over Heterogeneous Academic Hypernetwork

Ronghua Liang, Xiaorui Jiang

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review


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 publicationProceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)
Number of pages7
Publication statusPublished - 12 Feb 2016
Externally publishedYes
Event30th AAAI Conference on Artificial Intelligence - Phoenix, United States
Duration: 12 Feb 201617 Feb 2016
Conference number: 30


Conference30th AAAI Conference on Artificial Intelligence
Country/TerritoryUnited States
Internet address


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