FOCAS: Penalising friendly citations to improve author ranking

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Silva, J (Author) Other; Aparicio, D (Author) Other; Pedro Ribeiro (Author) FCUP; Silva, F (Author) FCUP

Conference proceedings International

Title: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages: 1852-1860

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-ZAW

DOI: 10.1145/3341105.3373991

Abstract (EN): Scientific impact is commonly associated with the number of citations received. However, an author can easily boost his own citation count by (i) publishing articles that cite his own previous work (self-citations), (ii) having co-authors citing his work (co-author citations), or (iii) exchanging citations with authors from other research groups (reciprocated citations). Even though these friendly citations inflate an author's perceived scientific impact, author ranking algorithms do not normally address them. They, at most, remove self-citations. Here we present Friends-Only Citations AnalySer (FOCAS), a method that identifies friendly citations and reduces their negative effect in author ranking algorithms. FOCAS combines the author citation network with the co-authorship network in order to measure author proximity and penalises citations between friendly authors. FOCAS is general and can be regarded as an independent module applied while running (any) PageRank-like author ranking algorithm. FOCAS can be tuned to use three different criteria, namely authors' distance, citation frequency, and citation recency, or combinations of these. We evaluate and compare FOCAS against eight state-of-the-art author ranking algorithms. We compare their rankings with a ground-truth of best paper awards. We test our hypothesis on a citation and co-authorship network comprised of seven Information Retrieval top-conferences. We observed that FOCAS improved author rankings by 25% on average and, in one case, leads to a gain of 46%.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9