A nonlinear collective credit allocation in scientific publications.

Autor/inn/en	Wang, Fenghua; Fan, Ying; Zeng, An; Di, Zengru
Titel	A nonlinear collective credit allocation in scientific publications.
Quelle	In: Scientometrics, (2019) 3, S.1655-1668 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-019-03107-x
Schlagwörter	Citation network; Credit allocation; Index
Abstract	Abstract Collaboration among researchers plays an important role in scientific discoveries, especially in multidisciplinary research. How to allocate credit reasonably to coauthors of a paper is a long-standing problem in the science of sciences. The collective credit allocation method (CCA method) proposed by Shen, H. W. and Barabási, A. L. provides a novel view to solve this problem, which measures the coauthors’ contribution to a paper based on the citation process by the scientific community. Nevertheless, the existing collective allocation method assigns equal weights to citing papers, which is sensitive to the malicious manipulation. In this paper, we propose a nonlinear collective credit allocation method (NCCA method) that assigns different strength to citing papers according to papers’ scientific impact when measuring papers’ similarity. Compared to the CCA method, we find that the NCCA method assigns more credits to Nobel laureates in the Nobel-winning papers. Moreover, the NCCA method is robust against random perturbations and the malicious manipulation in both Nobel-prize papers and ordinary papers. Furthermore, the collective credit allocation method can also modify h index.
Erfasst von	OLC
Update	2023/2/05