RelRank: A relevance-based author ranking algorithm for individual publication venues

Yu Zhang, Min Wang, Michael Zipperle, Alireza Abbasi, Massimiliano Tani

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Hiring appropriate editors, chairs and committee members for academic journals and conferences is challenging. It requires a targeted search for high profile scholars who are active in the field as well as in the publication venue. Many author-level metrics have been employed for this task, such as the h-index, PageRank and their variants. However, these metrics are global measures which evaluate authors’ productivity and impact without differentiating the publication venues. From the perspective of a venue, it is also important to have a localised metric which can specifically indicate the significance of academic authors for the particular venue. In this paper, we propose a relevance-based author ranking algorithm to measure the significance of authors to individual venues. Specifically, we develop a co-authorship network considering the author-venue relationship which integrates the statistical relevance of authors to individual venues. The RelRank, an improved PageRank algorithm embedding author relevance, is then proposed to rank authors for each venue. Extensive experiments are carried out to analyse the proposed RelRank in comparison with classic author-level metrics on three datasets of different research domains. We also evaluate the effectiveness of the RelRank and comparison metrics in recommending editorial boards of three venues using test data. Results demonstrate that the RelRank is able to identify not only the high profile scholars but also those who are particularly significant for individual venues.

Original languageEnglish
Article number103156
Pages (from-to)1-14
Number of pages14
JournalInformation Processing and Management
Issue number1
Publication statusPublished - Jan 2023
Externally publishedYes


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