13 research outputs found
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
Tunable and Growing Network Generation Model with Community Structures
Recent years have seen a growing interest in the modeling and simulation of
social networks to understand several social phenomena. Two important classes
of networks, small world and scale free networks have gained a lot of research
interest. Another important characteristic of social networks is the presence
of community structures. Many social processes such as information diffusion
and disease epidemics depend on the presence of community structures making it
an important property for network generation models to be incorporated. In this
paper, we present a tunable and growing network generation model with small
world and scale free properties as well as the presence of community
structures. The major contribution of this model is that the communities thus
created satisfy three important structural properties: connectivity within each
community follows power-law, communities have high clustering coefficient and
hierarchical community structures are present in the networks generated using
the proposed model. Furthermore, the model is highly robust and capable of
producing networks with a number of different topological characteristics
varying clustering coefficient and inter-cluster edges. Our simulation results
show that the model produces small world and scale free networks along with the
presence of communities depicting real world societies and social networks.Comment: Social Computing and Its Applications, SCA 13, Karlsruhe : Germany
(2013
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
A Biased Review of Sociophysics
Various aspects of recent sociophysics research are shortly reviewed:
Schelling model as an example for lack of interdisciplinary cooperation,
opinion dynamics, combat, and citation statistics as an example for strong
interdisciplinarity.Comment: 16 pages for J. Stat. Phys. including 2 figures and numerous
reference
Modelling Citation Networks
The distribution of the number of academic publications as a function of
citation count for a given year is remarkably similar from year to year. We
measure this similarity as a width of the distribution and find it to be
approximately constant from year to year. We show that simple citation models
fail to capture this behaviour. We then provide a simple three parameter
citation network model using a mixture of local and global search processes
which can reproduce the correct distribution over time. We use the citation
network of papers from the hep-th section of arXiv to test our model. For this
data, around 20% of citations use global information to reference recently
published papers, while the remaining 80% are found using local searches. We
note that this is consistent with other studies though our motivation is very
different from previous work. Finally, we also find that the fluctuations in
the size of an academic publication's bibliography is important for the model.
This is not addressed in most models and needs further work.Comment: 29 pages, 22 figure