576,145 research outputs found
Uncovering the dynamics of citations of scientific papers
We demonstrate a comprehensive framework that accounts for citation dynamics
of scientific papers and for the age distribution of references. We show that
citation dynamics of scientific papers is nonlinear and this nonlinearity has
far-reaching consequences, such as diverging citation distributions and runaway
papers. We propose a nonlinear stochastic dynamic model of citation dynamics
based on link copying/redirection mechanism. The model is fully calibrated by
empirical data and does not contain free parameters. This model can be a basis
for quantitative probabilistic prediction of citation dynamics of individual
papers and of the journal impact factor.Comment: 18 pages, 7 figure
On the Stability of Community Detection Algorithms on Longitudinal Citation Data
There are fundamental differences between citation networks and other classes
of graphs. In particular, given that citation networks are directed and
acyclic, methods developed primarily for use with undirected social network
data may face obstacles. This is particularly true for the dynamic development
of community structure in citation networks. Namely, it is neither clear when
it is appropriate to employ existing community detection approaches nor is it
clear how to choose among existing approaches. Using simulated data, we attempt
to clarify the conditions under which one should use existing methods and which
of these algorithms is appropriate in a given context. We hope this paper will
serve as both a useful guidepost and an encouragement to those interested in
the development of more targeted approaches for use with longitudinal citation
data.Comment: 17 pages, 7 figures, presenting at Applications of Social Network
Analysis 2009, ETH Zurich Edit, August 17, 2009: updated abstract, figures,
text clarification
Dynamic Data Citation Service-Subset Tool for Operational Data Management
In earth observation and climatological sciences, data and their data services grow on a daily
basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but
also by continuous meteorological in situ observations. In order to reuse such data, especially data
fragments as well as their data services in a collaborative and reproducible manner by citing the origin
source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact
version, precise time information, parameter, and names of the dataset used. A manual process would
make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to
obtain. Data in climate research are in most cases multidimensional, structured grid data that can
change partially over time. The citation of such evolving content requires the approach of "dynamic
data citation". The applied approach is based on associating queries with persistent identifiers. These
queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the
time frame with a start and end date, which are automatically included in the metadata of the newly
generated subset and thus represent the information about the data history, the data provenance,
which has to be established in data repository ecosystems. The Research Data Alliance Data Citation
Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of
the art from existing citation and data management concepts and developed the scalable dynamic
data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria
(CCCA) has implemented the given recommendations and offers since 2017 an operational service
on dynamic data citation on climate scenario data. With the consciousness that the objective of this
topic brings a lot of dependencies on bibliographic citation research which is still under discussion,
the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like
characteristics of data, formats, software environment, and usage behavior. The current effort beyond
spreading made experiences will be the scalability of the implementation, e.g., towards the potential
of an Open Data Cube solution
Intrinsically Dynamic Network Communities
Community finding algorithms for networks have recently been extended to
dynamic data. Most of these recent methods aim at exhibiting community
partitions from successive graph snapshots and thereafter connecting or
smoothing these partitions using clever time-dependent features and sampling
techniques. These approaches are nonetheless achieving longitudinal rather than
dynamic community detection. We assume that communities are fundamentally
defined by the repetition of interactions among a set of nodes over time.
According to this definition, analyzing the data by considering successive
snapshots induces a significant loss of information: we suggest that it blurs
essentially dynamic phenomena - such as communities based on repeated
inter-temporal interactions, nodes switching from a community to another across
time, or the possibility that a community survives while its members are being
integrally replaced over a longer time period. We propose a formalism which
aims at tackling this issue in the context of time-directed datasets (such as
citation networks), and present several illustrations on both empirical and
synthetic dynamic networks. We eventually introduce intrinsically dynamic
metrics to qualify temporal community structure and emphasize their possible
role as an estimator of the quality of the community detection - taking into
account the fact that various empirical contexts may call for distinct
`community' definitions and detection criteria.Comment: 27 pages, 11 figure
Bibliometric cartography of information retrieval research by using co-word analysis
The aim of this study is to map the intellectual structure of the field of Information Retrieval (IR) during the period of 1987-1997. Co-word analysis was employed to reveal patterns and trends in the IR field by measuring the association strengths of terms representative of relevant publications or other texts produced in IR field. Data were collected from Science Citation Index (SCI) and Social Science Citation Index (SSCI) for the period of 1987-1997. In addition to the keywords added by the SCI and SSCI databases, other important keywords were extracted from titles and abstracts manually. These keywords were further standardized using vocabulary control tools. In order to trace the dynamic changes of the IR field, the whole 11-year period was further separated into two consecutive periods: 1987-1991 and 1992-1997. The results show that the IR field has some established research themes and it also changes rapidly to embrace new themes
Quantum-Critical Behavior in a Two-Layer Antiferromagnet
We analyze quantum Monte Carlo data in the vicinity of the quantum transition
between a Neel state and a quantum paramagnet in a two-layer, square lattice
spin 1/2 Heisenberg antiferromagnet. The real-space correlation function and
the universal amplitude ratio of the structure factor and the dynamic
susceptibility show clear evidence of quantum critical behavior at low
temperatures. The numerical results are in good quantitative agreement with
calculations for the non-linear sigma model. A discrepancy,
reported earlier, between the critical properties of the antiferromagnet and
the sigma model is resolved. We also discuss the values of prefactors of the
dynamic susceptibility and the structure factor in a single layer
antiferromagnet at low .Comment: 11 pages, REVtex file, 5 figures in a uuencoded, gziped file. One
citation added
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