242 research outputs found
Modeling citation concentration through a mixture of Leimkuhler curves
When a graphical representation of the cumulative percentage of total
citations to articles, ordered from most cited to least cited, is plotted
against the cumulative percentage of articles, we obtain a Leimkuhler curve. In
this study, we noticed that standard Leimkuhler functions may not be sufficient
to provide accurate fits to various empirical informetrics data. Therefore, we
introduce a new approach to Leimkuhler curves by fitting a known probability
density function to the initial Leimkuhler curve, taking into account the
presence of a heterogeneity factor. As a significant contribution to the
existing literature, we introduce a pair of mixture distributions (called PG
and PIG) to bibliometrics. In addition, we present closed-form expressions for
Leimkuhler curves. {Some measures of citation concentration are examined
empirically for the basic models (based on the Power {and Pareto
distributions}) and the mixed models derived from {these}.} An application to
two sources of informetric data was conducted to see how the mixing models
outperform the standard basic models. The different models were fitted using
non-linear least squares estimation.Comment: 21 pages, 2 figures, 2 table
Microscopic Aspects of Stretched Exponential Relaxation (SER) in Homogeneous Molecular and Network Glasses and Polymers
Because the theory of SER is still a work in progress, the phenomenon itself
can be said to be the oldest unsolved problem in science, as it started with
Kohlrausch in 1847. Many electrical and optical phenomena exhibit SER with
probe relaxation I(t) ~ exp[-(t/{\tau}){\beta}], with 0 < {\beta} < 1. Here
{\tau} is a material-sensitive parameter, useful for discussing chemical
trends. The "shape" parameter {\beta} is dimensionless and plays the role of a
non-equilibrium scaling exponent; its value, especially in glasses, is both
practically useful and theoretically significant. The mathematical complexity
of SER is such that rigorous derivations of this peculiar function were not
achieved until the 1970's. The focus of much of the 1970's pioneering work was
spatial relaxation of electronic charge, but SER is a universal phenomenon, and
today atomic and molecular relaxation of glasses and deeply supercooled liquids
provide the most reliable data. As the data base grew, the need for a
quantitative theory increased; this need was finally met by the
diffusion-to-traps topological model, which yields a remarkably simple
expression for the shape parameter {\beta}, given by d*/(d* + 2). At first
sight this expression appears to be identical to d/(d + 2), where d is the
actual spatial dimensionality, as originally derived. The original model,
however, failed to explain much of the data base. Here the theme of earlier
reviews, based on the observation that in the presence of short-range forces
only d* = d = 3 is the actual spatial dimensionality, while for mixed short-
and long-range forces, d* = fd = d/2, is applied to four new spectacular
examples, where it turns out that SER is useful not only for purposes of
quality control, but also for defining what is meant by a glass in novel
contexts. (Please see full abstract in main text
Theories of Informetrics and Scholarly Communication
Scientometrics have become an essential element in the practice and evaluation of science and research, including both the evaluation of individuals and national assessment exercises. Yet, researchers and practitioners in this field have lacked clear theories to guide their work. As early as 1981, then doctoral student Blaise Cronin published The need for a theory of citing - a call to arms for the fledgling scientometric community to produce foundational theories upon which the work of the field could be based. More than three decades later, the time has come to reach out the field again and ask how they have responded to this call. This book compiles the foundational theories that guide informetrics and scholarly communication research. It is a much needed compilation by leading scholars in the field that gathers together the theories that guide our understanding of authorship, citing, and impact
Contrasting Views of Complexity and Their Implications For Network-Centric Infrastructures
There exists a widely recognized need to better understand
and manage complex “systems of systems,” ranging from
biology, ecology, and medicine to network-centric technologies.
This is motivating the search for universal laws of highly evolved
systems and driving demand for new mathematics and methods
that are consistent, integrative, and predictive. However, the theoretical
frameworks available today are not merely fragmented
but sometimes contradictory and incompatible. We argue that
complexity arises in highly evolved biological and technological
systems primarily to provide mechanisms to create robustness.
However, this complexity itself can be a source of new fragility,
leading to “robust yet fragile” tradeoffs in system design. We
focus on the role of robustness and architecture in networked
infrastructures, and we highlight recent advances in the theory
of distributed control driven by network technologies. This view
of complexity in highly organized technological and biological systems
is fundamentally different from the dominant perspective in
the mainstream sciences, which downplays function, constraints,
and tradeoffs, and tends to minimize the role of organization and
design
Theories of Informetrics and Scholarly Communication
Scientometrics have become an essential element in the practice and evaluation of science and research, including both the evaluation of individuals and national assessment exercises. Yet, researchers and practitioners in this field have lacked clear theories to guide their work. As early as 1981, then doctoral student Blaise Cronin published "The need for a theory of citing" —a call to arms for the fledgling scientometric community to produce foundational theories upon which the work of the field could be based. More than three decades later, the time has come to reach out the field again and ask how they have responded to this call.
This book compiles the foundational theories that guide informetrics and scholarly communication research. It is a much needed compilation by leading scholars in the field that gathers together the theories that guide our understanding of authorship, citing, and impact
More "normal" than normal: scaling distributions and complex systems
One feature of many naturally occurring or engineered complex systems is tremendous variability in event sizes. To account for it, the behavior of these systems is often described using power law relationships or scaling distributions, which tend to be viewed as "exotic" because of their unusual properties (e.g., infinite moments). An alternate view is based on mathematical, statistical, and data-analytic arguments and suggests that scaling distributions should be viewed as "more normal than normal". In support of this latter view that has been advocated by Mandelbrot for the last 40 years, we review in this paper some relevant results from probability theory and illustrate a powerful statistical approach for deciding whether the variability associated with observed event sizes is consistent with an underlying Gaussian-type (finite variance) or scaling-type (infinite variance) distribution. We contrast this approach with traditional model fitting techniques and discuss its implications for future modeling of complex systems
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Models Performance Issues in Parallel Computing for Information Retrieval
The skewness of computer science
Computer science is a relatively young discipline combining science,
engineering, and mathematics. The main flavors of computer science research
involve the theoretical development of conceptual models for the different
aspects of computing and the more applicative building of software artifacts
and assessment of their properties. In the computer science publication
culture, conferences are an important vehicle to quickly move ideas, and
journals often publish deeper versions of papers already presented at
conferences. These peculiarities of the discipline make computer science an
original research field within the sciences, and, therefore, the assessment of
classical bibliometric laws is particularly important for this field. In this
paper, we study the skewness of the distribution of citations to papers
published in computer science publication venues (journals and conferences). We
find that the skewness in the distribution of mean citedness of different
venues combines with the asymmetry in citedness of articles in each venue,
resulting in a highly asymmetric citation distribution with a power law tail.
Furthermore, the skewness of conference publications is more pronounced than
the asymmetry of journal papers. Finally, the impact of journal papers, as
measured with bibliometric indicators, largely dominates that of proceeding
papers.Comment: I applied the goodness-of-fit methodology proposed in: A. Clauset, C.
R. Shalizi, M. E. J. Newman. Power-law distributions in empirical data. SIAM
Review 51, 661-703 (2009
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