297 research outputs found
Life, Death and Preferential Attachment
Scientific communities are characterized by strong stratification. The highly
skewed frequency distribution of citations of published scientific papers
suggests a relatively small number of active, cited papers embedded in a sea of
inactive and uncited papers. We propose an analytically soluble model which
allows for the death of nodes. This model provides an excellent description of
the citation distributions for live and dead papers in the SPIRES database.
Further, this model suggests a novel and general mechanism for the generation
of power law distributions in networks whenever the fraction of active nodes is
small.Comment: 5 pages, 2 figure
Correlations in Networks associated to Preferential Growth
Combinations of random and preferential growth for both on-growing and
stationary networks are studied and a hierarchical topology is observed. Thus
for real world scale-free networks which do not exhibit hierarchical features
preferential growth is probably not the main ingredient in the growth process.
An example of such real world networks includes the protein-protein interaction
network in yeast, which exhibits pronounced anti-hierarchical features.Comment: 4 pages, 4 figure
The Geography of Scientific Productivity: Scaling in U.S. Computer Science
Here we extract the geographical addresses of authors in the Citeseer
database of computer science papers. We show that the productivity of research
centres in the United States follows a power-law regime, apart from the most
productive centres for which we do not have enough data to reach definite
conclusions. To investigate the spatial distribution of computer science
research centres in the United States, we compute the two-point correlation
function of the spatial point process and show that the observed power-laws do
not disappear even when we change the physical representation from geographical
space to cartogram space. Our work suggests that the effect of physical
location poses a challenge to ongoing efforts to develop realistic models of
scientific productivity. We propose that the introduction of a fine scale
geography may lead to more sophisticated indicators of scientific output.Comment: 6 pages, 3 figures; minor change
A quantitative analysis of measures of quality in science
Condensing the work of any academic scientist into a one-dimensional measure
of scientific quality is a difficult problem. Here, we employ Bayesian
statistics to analyze several different measures of quality. Specifically, we
determine each measure's ability to discriminate between scientific authors.
Using scaling arguments, we demonstrate that the best of these measures require
approximately 50 papers to draw conclusions regarding long term scientific
performance with usefully small statistical uncertainties. Further, the
approach described here permits the value-free (i.e., statistical) comparison
of scientists working in distinct areas of science.Comment: 11 pages, 8 figures, 4 table
Runaway Events Dominate the Heavy Tail of Citation Distributions
Statistical distributions with heavy tails are ubiquitous in natural and
social phenomena. Since the entries in heavy tail have disproportional
significance, the knowledge of its exact shape is very important. Citations of
scientific papers form one of the best-known heavy tail distributions. Even in
this case there is a considerable debate whether citation distribution follows
the log-normal or power-law fit. The goal of our study is to solve this debate
by measuring citation distribution for a very large and homogeneous data. We
measured citation distribution for 418,438 Physics papers published in
1980-1989 and cited by 2008. While the log-normal fit deviates too strong from
the data, the discrete power-law function with the exponent does
better and fits 99.955% of the data. However, the extreme tail of the
distribution deviates upward even from the power-law fit and exhibits a
dramatic "runaway" behavior. The onset of the runaway regime is revealed
macroscopically as the paper garners 1000-1500 citations, however the
microscopic measurements of autocorrelation in citation rates are able to
predict this behavior in advance.Comment: 6 pages, 5 Figure
Consensuality of Peer Nominations Among Scientists
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69069/2/10.1177_107554708200400210.pd
Inheritance patterns in citation networks reveal scientific memes
Memes are the cultural equivalent of genes that spread across human culture
by means of imitation. What makes a meme and what distinguishes it from other
forms of information, however, is still poorly understood. Our analysis of
memes in the scientific literature reveals that they are governed by a
surprisingly simple relationship between frequency of occurrence and the degree
to which they propagate along the citation graph. We propose a simple
formalization of this pattern and we validate it with data from close to 50
million publication records from the Web of Science, PubMed Central, and the
American Physical Society. Evaluations relying on human annotators, citation
network randomizations, and comparisons with several alternative approaches
confirm that our formula is accurate and effective, without a dependence on
linguistic or ontological knowledge and without the application of arbitrary
thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical
Review
Characterizing Interdisciplinarity of Researchers and Research Topics Using Web Search Engines
Researchers' networks have been subject to active modeling and analysis.
Earlier literature mostly focused on citation or co-authorship networks
reconstructed from annotated scientific publication databases, which have
several limitations. Recently, general-purpose web search engines have also
been utilized to collect information about social networks. Here we
reconstructed, using web search engines, a network representing the relatedness
of researchers to their peers as well as to various research topics.
Relatedness between researchers and research topics was characterized by
visibility boost-increase of a researcher's visibility by focusing on a
particular topic. It was observed that researchers who had high visibility
boosts by the same research topic tended to be close to each other in their
network. We calculated correlations between visibility boosts by research
topics and researchers' interdisciplinarity at individual level (diversity of
topics related to the researcher) and at social level (his/her centrality in
the researchers' network). We found that visibility boosts by certain research
topics were positively correlated with researchers' individual-level
interdisciplinarity despite their negative correlations with the general
popularity of researchers. It was also found that visibility boosts by
network-related topics had positive correlations with researchers' social-level
interdisciplinarity. Research topics' correlations with researchers'
individual- and social-level interdisciplinarities were found to be nearly
independent from each other. These findings suggest that the notion of
"interdisciplinarity" of a researcher should be understood as a
multi-dimensional concept that should be evaluated using multiple assessment
means.Comment: 20 pages, 7 figures. Accepted for publication in PLoS On
Communities and patterns of scientific collaboration in Business and Management
This is the author's accepted version of this article deposited at arXiv (arXiv:1006.1788v2 [physics.soc-ph]) and subsequently published in Scientometrics October 2011, Volume 89, Issue 1, pp 381-396. The final publication is available at link.springer.com http://link.springer.com/article/10.1007%2Fs11192-011-0439-1Author's note: 17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full pape
Community structure and patterns of scientific collaboration in Business and Management
This is the author's accepted version of this article deposited at arXiv (arXiv:1006.1788v2 [physics.soc-ph]) and subsequently published in Scientometrics October 2011, Volume 89, Issue 1, pp 381-396. The final publication is available at link.springer.com http://link.springer.com/article/10.1007%2Fs11192-011-0439-1Author's note: 17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full pape
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