488 research outputs found
Module identification in bipartite and directed networks
Modularity is one of the most prominent properties of real-world complex
networks. Here, we address the issue of module identification in two important
classes of networks: bipartite networks and directed unipartite networks. Nodes
in bipartite networks are divided into two non-overlapping sets, and the links
must have one end node from each set. Directed unipartite networks only have
one type of nodes, but links have an origin and an end. We show that directed
unipartite networks can be conviniently represented as bipartite networks for
module identification purposes. We report a novel approach especially suited
for module detection in bipartite networks, and define a set of random networks
that enable us to validate the new approach
Signatures of currency vertices
Many real-world networks have broad degree distributions. For some systems,
this means that the functional significance of the vertices is also broadly
distributed, in other cases the vertices are equally significant, but in
different ways. One example of the latter case is metabolic networks, where the
high-degree vertices -- the currency metabolites -- supply the molecular groups
to the low-degree metabolites, and the latter are responsible for the
higher-order biological function, of vital importance to the organism. In this
paper, we propose a generalization of currency metabolites to currency
vertices. We investigate the network structural characteristics of such
systems, both in model networks and in some empirical systems. In addition to
metabolic networks, we find that a network of music collaborations and a
network of e-mail exchange could be described by a division of the vertices
into currency vertices and others.Comment: to appear in Journal of the Physical Society of Japa
Size reduction of complex networks preserving modularity
The ubiquity of modular structure in real-world complex networks is being the
focus of attention in many trials to understand the interplay between network
topology and functionality. The best approaches to the identification of
modular structure are based on the optimization of a quality function known as
modularity. However this optimization is a hard task provided that the
computational complexity of the problem is in the NP-hard class. Here we
propose an exact method for reducing the size of weighted (directed and
undirected) complex networks while maintaining invariant its modularity. This
size reduction allows the heuristic algorithms that optimize modularity for a
better exploration of the modularity landscape. We compare the modularity
obtained in several real complex-networks by using the Extremal Optimization
algorithm, before and after the size reduction, showing the improvement
obtained. We speculate that the proposed analytical size reduction could be
extended to an exact coarse graining of the network in the scope of real-space
renormalization.Comment: 14 pages, 2 figure
Functional cartography of complex metabolic networks
High-throughput techniques are leading to an explosive growth in the size of
biological databases and creating the opportunity to revolutionize our
understanding of life and disease. Interpretation of these data remains,
however, a major scientific challenge. Here, we propose a methodology that
enables us to extract and display information contained in complex networks.
Specifically, we demonstrate that one can (i) find functional modules in
complex networks, and (ii) classify nodes into universal roles according to
their pattern of intra- and inter-module connections. The method thus yields a
``cartographic representation'' of complex networks. Metabolic networks are
among the most challenging biological networks and, arguably, the ones with
more potential for immediate applicability. We use our method to analyze the
metabolic networks of twelve organisms from three different super-kingdoms. We
find that, typically, 80% of the nodes are only connected to other nodes within
their respective modules, and that nodes with different roles are affected by
different evolutionary constraints and pressures. Remarkably, we find that
low-degree metabolites that connect different modules are more conserved than
hubs whose links are mostly within a single module.Comment: 17 pages, 4 figures. Go to http://amaral.northwestern.edu for the PDF
file of the reprin
Network-Based Models for Social Recommender Systems
With the overwhelming online products available in recent years, there is an increasing need to filter and deliver relevant personalized advice for users. Recommender systems solve this problem by modelling and predicting individual preferences for a great variety of items such as movies, books or research articles. In this chapter, we explore rigorous network-based models that outperform leading approaches for recommendation. The network models we consider are based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. The accurate prediction of individual user preferences over items can be accomplished by different methodologies, such as Monte Carlo sampling or Expectation-Maximization methods, the latter resulting in a scalable algorithm which is suitable for large datasets
Obtaining Communities with a Fitness Growth Process
The study of community structure has been a hot topic of research over the
last years. But, while successfully applied in several areas, the concept lacks
of a general and precise notion. Facts like the hierarchical structure and
heterogeneity of complex networks make it difficult to unify the idea of
community and its evaluation. The global functional known as modularity is
probably the most used technique in this area. Nevertheless, its limits have
been deeply studied. Local techniques as the ones by Lancichinetti et al. and
Palla et al. arose as an answer to the resolution limit and degeneracies that
modularity has.
Here we start from the algorithm by Lancichinetti et al. and propose a unique
growth process for a fitness function that, while being local, finds a
community partition that covers the whole network, updating the scale parameter
dynamically. We test the quality of our results by using a set of benchmarks of
heterogeneous graphs. We discuss alternative measures for evaluating the
community structure and, in the light of them, infer possible explanations for
the better performance of local methods compared to global ones in these cases
A new four-point probe design to measure conductivity in polymeric thin films
In the development of new conducting polymers applications,the conductivity measurement is still a challenge, specially for extremely thin samples as the ones obtained by CVD. This study shows the design of a novel four-point probe for conductivity characterization of polypirrole thin films synthesized by plasma enhanced polymerization.The system possesses the minimal distance possible among electrodes, together with a high ratio of electrode length to spacing to enhance the electrical response. The four-point probe has been fabricated in a printed circuit board, which offers some advantages such as non-damaging samples, low cost or repeatability in the analysis measurements
Communication and optimal hierarchical networks
We study a general and simple model for communication processes. In the
model, agents in a network (in particular, an organization) interchange
information packets following simple rules that take into account the limited
capability of the agents to deal with packets and the cost associated to the
existence of open communication channels. Due to the limitation in the
capability, the network collapses under certain conditions. We focus on when
the collapse occurs for hierarchical networks and also on the influence of the
flatness or steepness of the structure. We find that the need for hierarchy is
related to the existence of costly connections.Comment: 7 pages, 2 figures. NATO ARW on Econophysic
Communication in networks with hierarchical branching
We present a simple model of communication in networks with hierarchical
branching. We analyze the behavior of the model from the viewpoint of critical
systems under different situations. For certain values of the parameters, a
continuous phase transition between a sparse and a congested regime is observed
and accurately described by an order parameter and the power spectra. At the
critical point the behavior of the model is totally independent of the number
of hierarchical levels. Also scaling properties are observed when the size of
the system varies. The presence of noise in the communication is shown to break
the transition. Despite the simplicity of the model, the analytical results are
a useful guide to forecast the main features of real networks.Comment: 4 pages, 3 figures. Final version accepted in PR
Immunization of networks with community structure
In this study, an efficient method to immunize modular networks (i.e.,
networks with community structure) is proposed. The immunization of networks
aims at fragmenting networks into small parts with a small number of removed
nodes. Its applications include prevention of epidemic spreading, intentional
attacks on networks, and conservation of ecosystems. Although preferential
immunization of hubs is efficient, good immunization strategies for modular
networks have not been established. On the basis of an immunization strategy
based on the eigenvector centrality, we develop an analytical framework for
immunizing modular networks. To this end, we quantify the contribution of each
node to the connectivity in a coarse-grained network among modules. We verify
the effectiveness of the proposed method by applying it to model and real
networks with modular structure.Comment: 3 figures, 1 tabl
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