Name
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 hypemetwork, 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 benchmark* environment based on the ACL Anthology Network. |
Year | Venue | Field |
|---|---|---|
2016 | THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Scientific literature,Ranking,Computer science,Flat organization,Multinomial distribution,Exploit,Artificial intelligence,Machine learning,Benchmarking |
DocType | Citations | PageRank |
Conference | 3 | 0.38 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronghua Liang | 1 | 376 | 42.60 |
| Xiaorui Jiang | 2 | 62 | 6.90 |