53 research outputs found
Scale-free download network for publications
The scale-free power-law behavior of the statistics of the download frequency
of publications has been, for the first time, reported. The data of the
download frequency of publications are taken from a well-constructed web page
in the field of economic physics (http://www.unifr.ch/econophysics/). The
Zipf-law analysis and the Tsallis entropy method were used to fit the download
frequency. It was found that the power-law exponent of rank-ordered frequency
distribution is which is consistent with the
power-law exponent for the cumulated frequency
distributions. Preferential attachment model of Barabasi and Albert network has
been used to explain the download network.Comment: 3 pages, 2 figure
Citation Networks in High Energy Physics
The citation network constituted by the SPIRES data base is investigated
empirically. The probability that a given paper in the SPIRES data base has
citations is well described by simple power laws, ,
with for less than 50 citations and for 50 or more citations. Two models are presented that both represent the
data well, one which generates power laws and one which generates a stretched
exponential. It is not possible to discriminate between these models on the
present empirical basis. A consideration of citation distribution by subfield
shows that the citation patterns of high energy physics form a remarkably
homogeneous network. Further, we utilize the knowledge of the citation
distributions to demonstrate the extreme improbability that the citation
records of selected individuals and institutions have been obtained by a random
draw on the resulting distribution.Comment: 9 pages, 6 figures, 2 table
Internal avalanches in a pile of superconducting vortices
Using an array of miniature Hall probes, we monitored the spatiotemporal
variation of the internal magnetic induction in a superconducting niobium
sample during a slow sweep of external magnetic field. We found that a sizable
fraction of the increase in the local vortex population occurs in abrupt jumps.
The size distribution of these avalanches presents a power-law collapse on a
limited range. In contrast, at low temperatures and low fields, huge avalanches
with a typical size occur and the system does not display a well-defined
macroscopic critical current.Comment: 5 pages including 5 figure
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
Algebraic Distribution of Segmental Duplication Lengths in Whole-Genome Sequence Self-Alignments
Distributions of duplicated sequences from genome self-alignment are characterized, including forward and backward alignments in bacteria and eukaryotes. A Markovian process without auto-correlation should generate an exponential distribution expected from local effects of point mutation and selection on localised function; however, the observed distributions show substantial deviation from exponential form – they are roughly algebraic instead – suggesting a novel kind of long-distance correlation that must be non-local in origin
Beyond word frequency: Bursts, lulls, and scaling in the temporal distributions of words
Background: Zipf's discovery that word frequency distributions obey a power
law established parallels between biological and physical processes, and
language, laying the groundwork for a complex systems perspective on human
communication. More recent research has also identified scaling regularities in
the dynamics underlying the successive occurrences of events, suggesting the
possibility of similar findings for language as well.
Methodology/Principal Findings: By considering frequent words in USENET
discussion groups and in disparate databases where the language has different
levels of formality, here we show that the distributions of distances between
successive occurrences of the same word display bursty deviations from a
Poisson process and are well characterized by a stretched exponential (Weibull)
scaling. The extent of this deviation depends strongly on semantic type -- a
measure of the logicality of each word -- and less strongly on frequency. We
develop a generative model of this behavior that fully determines the dynamics
of word usage.
Conclusions/Significance: Recurrence patterns of words are well described by
a stretched exponential distribution of recurrence times, an empirical scaling
that cannot be anticipated from Zipf's law. Because the use of words provides a
uniquely precise and powerful lens on human thought and activity, our findings
also have implications for other overt manifestations of collective human
dynamics
What we need to know before speaking on Climate Change and Global Warming
Workshop on Energy Greenhouse Gases & Environment, Porto, 200
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