Exploiting heterogeneous scientific literature networks to combat ranking bias: Evidence from the computational linguistics area

Xiaorui Jiang, Xiaoping Sun, Zhe Yang, Hai Zhuge, Jianmin Yao

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph‐based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph‐based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less‐biased ranking than previous methods. MutualRank provides a unified model that involves both intra‐ and inter‐network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well‐known universities and two well‐known textbooks. The experimental results show that MutualRank greatly outperforms the state‐of‐the‐art competitors, including PageRank, HITS, CoRank, Future Rank, and P‐Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.
Original languageEnglish
Pages (from-to)1679-1702
Number of pages24
JournalJournal of the Association for Information Science and Technology
Volume67
Issue number7
Early online date24 Jun 2015
DOIs
Publication statusPublished - Jul 2016
Externally publishedYes

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