54,635 research outputs found
Efficiency of Scale-Free Networks: Error and Attack Tolerance
The concept of network efficiency, recently proposed to characterize the
properties of small-world networks, is here used to study the effects of errors
and attacks on scale-free networks. Two different kinds of scale-free networks,
i.e. networks with power law P(k), are considered: 1) scale-free networks with
no local clustering produced by the Barabasi-Albert model and 2) scale-free
networks with high clustering properties as in the model by Klemm and Eguiluz,
and their properties are compared to the properties of random graphs
(exponential graphs). By using as mathematical measures the global and the
local efficiency we investigate the effects of errors and attacks both on the
global and the local properties of the network. We show that the global
efficiency is a better measure than the characteristic path length to describe
the response of complex networks to external factors. We find that, at variance
with random graphs, scale-free networks display, both on a global and on a
local scale, a high degree of error tolerance and an extreme vulnerability to
attacks. In fact, the global and the local efficiency are unaffected by the
failure of some randomly chosen nodes, though they are extremely sensititive to
the removal of the few nodes which play a crucial role in maintaining the
network's connectivity.Comment: 23 pages, 10 figure
Sampling motif-constrained ensembles of networks
The statistical significance of network properties is conditioned on null
models which satisfy spec- ified properties but that are otherwise random.
Exponential random graph models are a principled theoretical framework to
generate such constrained ensembles, but which often fail in practice, either
due to model inconsistency, or due to the impossibility to sample networks from
them. These problems affect the important case of networks with prescribed
clustering coefficient or number of small connected subgraphs (motifs). In this
paper we use the Wang-Landau method to obtain a multicanonical sampling that
overcomes both these problems. We sample, in polynomial time, net- works with
arbitrary degree sequences from ensembles with imposed motifs counts. Applying
this method to social networks, we investigate the relation between
transitivity and homophily, and we quantify the correlation between different
types of motifs, finding that single motifs can explain up to 60% of the
variation of motif profiles.Comment: Updated version, as published in the journal. 7 pages, 5 figures, one
Supplemental Materia
A statistical model for brain networks inferred from large-scale electrophysiological signals
Network science has been extensively developed to characterize structural
properties of complex systems, including brain networks inferred from
neuroimaging data. As a result of the inference process, networks estimated
from experimentally obtained biological data, represent one instance of a
larger number of realizations with similar intrinsic topology. A modeling
approach is therefore needed to support statistical inference on the bottom-up
local connectivity mechanisms influencing the formation of the estimated brain
networks. We adopted a statistical model based on exponential random graphs
(ERGM) to reproduce brain networks, or connectomes, estimated by spectral
coherence between high-density electroencephalographic (EEG) signals. We
validated this approach in a dataset of 108 healthy subjects during eyes-open
(EO) and eyes-closed (EC) resting-state conditions. Results showed that the
tendency to form triangles and stars, reflecting clustering and node
centrality, better explained the global properties of the EEG connectomes as
compared to other combinations of graph metrics. Synthetic networks generated
by this model configuration replicated the characteristic differences found in
brain networks, with EO eliciting significantly higher segregation in the alpha
frequency band (8-13 Hz) as compared to EC. Furthermore, the fitted ERGM
parameter values provided complementary information showing that clustering
connections are significantly more represented from EC to EO in the alpha
range, but also in the beta band (14-29 Hz), which is known to play a crucial
role in cortical processing of visual input and externally oriented attention.
These findings support the current view of the brain functional segregation and
integration in terms of modules and hubs, and provide a statistical approach to
extract new information on the (re)organizational mechanisms in healthy and
diseased brains.Comment: Due to the limitation "The abstract field cannot be longer than 1,920
characters", the abstract appearing here is slightly shorter than that in the
PDF fil
Networks based on collisions among mobile agents
We investigate in detail a recent model of colliding mobile agents [Phys.
Rev. Lett.~96, 088702], used as an alternative approach to construct evolving
networks of interactions formed by the collisions governed by suitable
dynamical rules. The system of mobile agents evolves towards a quasi-stationary
state which is, apart small fluctuations, well characterized by the density of
the system and the residence time of the agents. The residence time defines a
collision rate and by varying the collision rate, the system percolates at a
critical value, with the emergence of a giant cluster whose critical exponents
are the ones of two-dimensional percolation. Further, the degree and clustering
coefficient distributions and the average path length show that the network
associated with such a system presents non-trivial features which, depending on
the collision rule, enables one not only to recover the main properties of
standard networks, such as exponential, random and scale-free networks, but
also to obtain other topological structures. Namely, we show a specific example
where the obtained structure has topological features which characterize
accurately the structure and evolution of social networks in different
contexts, ranging from networks of acquaintances to networks of sexual
contacts.Comment: 12 pages, 17 figure
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
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
Random Graph Generator for Bipartite Networks Modeling
The purpose of this article is to introduce a new iterative algorithm with
properties resembling real life bipartite graphs. The algorithm enables us to
generate wide range of random bigraphs, which features are determined by a set
of parameters.We adapt the advances of last decade in unipartite complex
networks modeling to the bigraph setting. This data structure can be observed
in several situations. However, only a few datasets are freely available to
test the algorithms (e.g. community detection, influential nodes
identification, information retrieval) which operate on such data. Therefore,
artificial datasets are needed to enhance development and testing of the
algorithms. We are particularly interested in applying the generator to the
analysis of recommender systems. Therefore, we focus on two characteristics
that, besides simple statistics, are in our opinion responsible for the
performance of neighborhood based collaborative filtering algorithms. The
features are node degree distribution and local clustering coeficient
Statistical Analysis of Bus Networks in India
Through the past decade the field of network science has established itself
as a common ground for the cross-fertilization of exciting inter-disciplinary
studies which has motivated researchers to model almost every physical system
as an interacting network consisting of nodes and links. Although public
transport networks such as airline and railway networks have been extensively
studied, the status of bus networks still remains in obscurity. In developing
countries like India, where bus networks play an important role in day-to-day
commutation, it is of significant interest to analyze its topological structure
and answer some of the basic questions on its evolution, growth, robustness and
resiliency. In this paper, we model the bus networks of major Indian cities as
graphs in \textit{L}-space, and evaluate their various statistical properties
using concepts from network science. Our analysis reveals a wide spectrum of
network topology with the common underlying feature of small-world property. We
observe that the networks although, robust and resilient to random attacks are
particularly degree-sensitive. Unlike real-world networks, like Internet, WWW
and airline, which are virtual, bus networks are physically constrained. The
presence of various geographical and economic constraints allow these networks
to evolve over time. Our findings therefore, throw light on the evolution of
such geographically and socio-economically constrained networks which will help
us in designing more efficient networks in the future.Comment: Submitted to PLOS ON
A spatial model for social networks
We study spatial embeddings of random graphs in which nodes are randomly
distributed in geographical space. We let the edge probability between any two
nodes to be dependent on the spatial distance between them and demonstrate that
this model captures many generic properties of social networks, including the
``small-world'' properties, skewed degree distribution, and most distinctively
the existence of community structures.Comment: To be published in Physica A (2005
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