16 research outputs found
Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index
Various factors are believed to govern the selection of references in
citation networks, but a precise, quantitative determination of their
importance has remained elusive. In this paper, we show that three factors can
account for the referencing pattern of citation networks for two topics, namely
"graphenes" and "complex networks", thus allowing one to reproduce the
topological features of the networks built with papers being the nodes and the
edges established by citations. The most relevant factor was content
similarity, while the other two - in-degree (i.e. citation counts) and {age of
publication} had varying importance depending on the topic studied. This
dependence indicates that additional factors could play a role. Indeed, by
intuition one should expect the reputation (or visibility) of authors and/or
institutions to affect the referencing pattern, and this is only indirectly
considered via the in-degree that should correlate with such reputation.
Because information on reputation is not readily available, we simulated its
effect on artificial citation networks considering two communities with
distinct fitness (visibility) parameters. One community was assumed to have
twice the fitness value of the other, which amounts to a double probability for
a paper being cited. While the h-index for authors in the community with larger
fitness evolved with time with slightly higher values than for the control
network (no fitness considered), a drastic effect was noted for the community
with smaller fitness
Modeling the clustering in citation networks
For the study of citation networks, a challenging problem is modeling the
high clustering. Existing studies indicate that the promising way to model the
high clustering is a copying strategy, i.e., a paper copies the references of
its neighbour as its own references. However, the line of models highly
underestimates the number of abundant triangles observed in real citation
networks and thus cannot well model the high clustering. In this paper, we
point out that the failure of existing models lies in that they do not capture
the connecting patterns among existing papers. By leveraging the knowledge
indicated by such connecting patterns, we further propose a new model for the
high clustering in citation networks. Experiments on two real world citation
networks, respectively from a special research area and a multidisciplinary
research area, demonstrate that our model can reproduce not only the power-law
degree distribution as traditional models but also the number of triangles, the
high clustering coefficient and the size distribution of co-citation clusters
as observed in these real networks
Complex scale-free networks with tunable power-law exponent and clustering
This article is made available through the Brunel Open Access Publishing Fund. It is distributed under a Creative Commons License (http://creativecommons.org/licenses/by/3.0/). Copyright @ 2013 Elsevier B.V.We introduce a network evolution process motivated by the network of citations in the scientific literature. In each iteration of the process a node is born and directed links are created from the new node to a set of target nodes already in the network. This set includes mm “ambassador” nodes and ll of each ambassador’s descendants where mm and ll are random variables selected from any choice of distributions plpl and qmqm. The process mimics the tendency of authors to cite varying numbers of papers included in the bibliographies of the other papers they cite. We show that the degree distributions of the networks generated after a large number of iterations are scale-free and derive an expression for the power-law exponent. In a particular case of the model where the number of ambassadors is always the constant mm and the number of selected descendants from each ambassador is the constant ll, the power-law exponent is (2l+1)/l(2l+1)/l. For this example we derive expressions for the degree distribution and clustering coefficient in terms of ll and mm. We conclude that the proposed model can be tuned to have the same power law exponent and clustering coefficient of a broad range of the scale-free distributions that have been studied empirically.EPSR
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
学術論文の引用ネットワークに対する時刻差を考慮した生成モデル
ISM Online Open House, 2021.6.18統計数理研究所オープンハウス(オンライン開催)、R3.6.18ポスター発
学術論文の引用ネットワークに対する生成モデル
ISM Online Open House, 2020.10.27統計数理研究所オープンハウス(オンライン開催)、R2.10.27ポスター発
Towards a more realistic citation model: The key role of research team sizes
We propose a new citation model which builds on the existing models that
explicitly or implicitly include "direct" and "indirect" (learning about a
cited paper's existence from references in another paper) citation mechanisms.
Our model departs from the usual, unrealistic assumption of uniform probability
of direct citation, in which initial differences in citation arise purely
randomly. Instead, we demonstrate that a two-mechanism model in which the
probability of direct citation is proportional to the number of authors on a
paper (team size) is able to reproduce the empirical citation distributions of
articles published in the field of astronomy remarkably well, and at different
points in time. Interpretation of our model is that the intrinsic citation
capacity, and hence the initial visibility of a paper, will be enhanced when
more people are intimately familiar with some work, favoring papers from larger
teams. While the intrinsic citation capacity cannot depend only on the team
size, our model demonstrates that it must be to some degree correlated with it,
and distributed in a similar way, i.e., having a power-law tail. Consequently,
our team-size model qualitatively explains the existence of a correlation
between the number of citations and the number of authors on a paper.Comment: Published in journal Entropy. Open access article available at
https://www.mdpi.com/journal/entrop