Citation Oriented AuthorRank for Scientific Publication Ranking
It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scie...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/9/4345 |
| _version_ | 1797505651139674112 |
|---|---|
| author | Jinsong Zhang Xiaozhong Liu |
| author_facet | Jinsong Zhang Xiaozhong Liu |
| author_sort | Jinsong Zhang |
| collection | DOAJ |
| description | It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (<i>p</i> < 0.001) than other baselines for publication ranking. |
| first_indexed | 2024-03-10T04:21:37Z |
| format | Article |
| id | doaj.art-1b247938eb8d40f392c428d7d621eb1d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:21:37Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1b247938eb8d40f392c428d7d621eb1d2023-11-23T07:47:45ZengMDPI AGApplied Sciences2076-34172022-04-01129434510.3390/app12094345Citation Oriented AuthorRank for Scientific Publication RankingJinsong Zhang0Xiaozhong Liu1School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Informatics, Computing and Engineering, Indiana University Bloomington, Blooming, IN 47405, USAIt is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (<i>p</i> < 0.001) than other baselines for publication ranking.https://www.mdpi.com/2076-3417/12/9/4345AuthorRankpublication rankingPageRank with Priorscitation context analysis |
| spellingShingle | Jinsong Zhang Xiaozhong Liu Citation Oriented AuthorRank for Scientific Publication Ranking Applied Sciences AuthorRank publication ranking PageRank with Priors citation context analysis |
| title | Citation Oriented AuthorRank for Scientific Publication Ranking |
| title_full | Citation Oriented AuthorRank for Scientific Publication Ranking |
| title_fullStr | Citation Oriented AuthorRank for Scientific Publication Ranking |
| title_full_unstemmed | Citation Oriented AuthorRank for Scientific Publication Ranking |
| title_short | Citation Oriented AuthorRank for Scientific Publication Ranking |
| title_sort | citation oriented authorrank for scientific publication ranking |
| topic | AuthorRank publication ranking PageRank with Priors citation context analysis |
| url | https://www.mdpi.com/2076-3417/12/9/4345 |
| work_keys_str_mv | AT jinsongzhang citationorientedauthorrankforscientificpublicationranking AT xiaozhongliu citationorientedauthorrankforscientificpublicationranking |