82 research outputs found
Weighted citation: An indicator of an article's prestige
We propose using the technique of weighted citation to measure an article's
prestige. The technique allocates a different weight to each reference by
taking into account the impact of citing journals and citation time intervals.
Weighted citation captures prestige, whereas citation counts capture
popularity. We compare the value variances for popularity and prestige for
articles published in the Journal of the American Society for Information
Science and Technology from 1998 to 2007, and find that the majority have
comparable status.Comment: 17 pages, 6 figure
Discovering author impact: A PageRank perspective
This article provides an alternative perspective for measuring author impact
by applying PageRank algorithm to a coauthorship network. A weighted PageRank
algorithm considering citation and coauthorship network topology is proposed.
We test this algorithm under different damping factors by evaluating author
impact in the informetrics research community. In addition, we also compare
this weighted PageRank with the h-index, citation, and program committee (PC)
membership of the International Society for Scientometrics and Informetrics
(ISSI) conferences. Findings show that this weighted PageRank algorithm
provides reliable results in measuring author impact.Comment: 17 pages, 5 figure
P-Rank: An indicator measuring prestige in heterogeneous scholarly networks
Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way
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