43,808 research outputs found
Evolving Clustered Random Networks
We propose a Markov chain simulation method to generate simple connected
random graphs with a specified degree sequence and level of clustering. The
networks generated by our algorithm are random in all other respects and can
thus serve as generic models for studying the impacts of degree distributions
and clustering on dynamical processes as well as null models for detecting
other structural properties in empirical networks
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
A Triclustering Approach for Time Evolving Graphs
This paper introduces a novel technique to track structures in time evolving
graphs. The method is based on a parameter free approach for three-dimensional
co-clustering of the source vertices, the target vertices and the time. All
these features are simultaneously segmented in order to build time segments and
clusters of vertices whose edge distributions are similar and evolve in the
same way over the time segments. The main novelty of this approach lies in that
the time segments are directly inferred from the evolution of the edge
distribution between the vertices, thus not requiring the user to make an a
priori discretization. Experiments conducted on a synthetic dataset illustrate
the good behaviour of the technique, and a study of a real-life dataset shows
the potential of the proposed approach for exploratory data analysis
On the formation of structure in growing networks
Based on the formation of triad junctions, the proposed mechanism generates
networks that exhibit extended rather than single power law behavior. Triad
formation guarantees strong neighborhood clustering and community-level
characteristics as the network size grows to infinity. The asymptotic behavior
is of interest in the study of directed networks in which (i) the formation of
links cannot be described according to the principle of preferential
attachment; (ii) the in-degree distribution fits a power law for nodes with a
high degree and an exponential form otherwise; (iii) clustering properties
emerge at multiple scales and depend on both the number of links that newly
added nodes establish and the probability of forming triads; and (iv) groups of
nodes form modules that feature less links to the rest of the nodes.Comment: 17 pages, 9 figures, we apply the proposed mechanism to generate
network realizations that resemble the degree distribution and clustering
properties of an empirical network with no directed cycles (i.e., when the
model parameter n=0), updated reference
Correlation effects in a simple model of small-world network
We analyze the effect of correlations in a simple model of small world
network by obtaining exact analytical expressions for the distribution of
shortest paths in the network. We enter correlations into a simple model with a
distinguished site, by taking the random connections to this site from an Ising
distribution. Our method shows how the transfer matrix technique can be used in
the new context of small world networks.Comment: 10 pages, 3 figure
Topology of the conceptual network of language
We define two words in a language to be connected if they express similar
concepts. The network of connections among the many thousands of words that
make up a language is important not only for the study of the structure and
evolution of languages, but also for cognitive science. We study this issue
quantitatively, by mapping out the conceptual network of the English language,
with the connections being defined by the entries in a Thesaurus dictionary. We
find that this network presents a small-world structure, with an amazingly
small average shortest path, and appears to exhibit an asymptotic scale-free
feature with algebraic connectivity distribution.Comment: 4 pages, 2 figures, Revte
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