69,637 research outputs found
Network-based ranking in social systems: three challenges
Ranking algorithms are pervasive in our increasingly digitized societies,
with important real-world applications including recommender systems, search
engines, and influencer marketing practices. From a network science
perspective, network-based ranking algorithms solve fundamental problems
related to the identification of vital nodes for the stability and dynamics of
a complex system. Despite the ubiquitous and successful applications of these
algorithms, we argue that our understanding of their performance and their
applications to real-world problems face three fundamental challenges: (i)
Rankings might be biased by various factors; (2) their effectiveness might be
limited to specific problems; and (3) agents' decisions driven by rankings
might result in potentially vicious feedback mechanisms and unhealthy systemic
consequences. Methods rooted in network science and agent-based modeling can
help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure
Citation Statistics
This is a report about the use and misuse of citation data in the assessment
of scientific research. The idea that research assessment must be done using
``simple and objective'' methods is increasingly prevalent today. The ``simple
and objective'' methods are broadly interpreted as bibliometrics, that is,
citation data and the statistics derived from them. There is a belief that
citation statistics are inherently more accurate because they substitute simple
numbers for complex judgments, and hence overcome the possible subjectivity of
peer review. But this belief is unfounded.Comment: This paper commented in: [arXiv:0910.3532], [arXiv:0910.3537],
[arXiv:0910.3543], [arXiv:0910.3546]. Rejoinder in [arXiv:0910.3548].
Published in at http://dx.doi.org/10.1214/09-STS285 the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Diffusion of scientific credits and the ranking of scientists
Recently, the abundance of digital data enabled the implementation of graph
based ranking algorithms that provide system level analysis for ranking
publications and authors. Here we take advantage of the entire Physical Review
publication archive (1893-2006) to construct authors' networks where weighted
edges, as measured from opportunely normalized citation counts, define a proxy
for the mechanism of scientific credit transfer. On this network we define a
ranking method based on a diffusion algorithm that mimics the spreading of
scientific credits on the network. We compare the results obtained with our
algorithm with those obtained by local measures such as the citation count and
provide a statistical analysis of the assignment of major career awards in the
area of Physics. A web site where the algorithm is made available to perform
customized rank analysis can be found at the address
http://www.physauthorsrank.orgComment: Revised version. 11 pages, 10 figures, 1 table. The portal to compute
the rankings of scientists is at http://www.physauthorsrank.or
SIGIR: scholar vs. scholars' interpretation
Google Scholar allows researchers to search through a free and extensive source of information on scientific publications. In this paper we show that within the limited context of SIGIR proceedings, the rankings created by Google Scholar are both significantly different and very negatively correlated with those of domain experts
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