117 research outputs found
Navigation and Cognition in Semantic Networks
Semantic memory is the cognitive system devoted to storage and retrieval of conceptual knowledge. Empirical data indicate that semantic memory is organized in a network structure. Everyday experience shows that word search and retrieval processes emerge providing fluent and coherent speech, i.e. are efficient and robust. Nonetheless, links between pairs of words in semantic memory encode a rich variety of relationships, and not merely category membership. To extract this information, we schematize a process based on uncorrelated random walks from node to node, which converge to a feature vectors network. This mechanism forces the emergence of semantic similarity, which implicitly encloses category structure. Interestingly, the degradation of the original structure has a dramatic impact on the topology of semantic network, whereas the dynamics upon it evidence much higher resilience. We define this problem in the framework of percolation theory
Antagonistic Structural Patterns in Complex Networks
Identifying and explaining the structure of complex networks at different
scales has become an important problem across disciplines. At the mesoscale,
modular architecture has attracted most of the attention. At the macroscale,
other arrangements --e.g. nestedness or core-periphery-- have been studied in
parallel, but to a much lesser extent. However, empirical evidence increasingly
suggests that characterizing a network with a unique pattern typology may be
too simplistic, since a system can integrate properties from distinct
organizations at different scales. Here, we explore the relationship between
some of those organizational patterns: two at the mesoscale (modularity and
in-block nestedness); and one at the macroscale (nestedness). We analytically
show that nestedness can be used to provide approximate bounds for modularity,
with exact results in an idealized scenario. Specifically, we show that
nestedness and modularity are antagonistic. Furthermore, we evince that
in-block nestedness provides a parsimonious transition between nested and
modular networks, taking properties of both. Far from a mere theoretical
exercise, understanding the boundaries that discriminate each architecture is
fundamental, to the extent modularity and nestedness are known to place heavy
constraints on the stability of several dynamical processes, specially in
ecology.Comment: 7 pages, 4 figures and 1 supplemental information fil
Discrete-time Markov chain approach to contact-based disease spreading in complex networks
Many epidemic processes in networks spread by stochastic contacts among their
connected vertices. There are two limiting cases widely analyzed in the physics
literature, the so-called contact process (CP) where the contagion is expanded
at a certain rate from an infected vertex to one neighbor at a time, and the
reactive process (RP) in which an infected individual effectively contacts all
its neighbors to expand the epidemics. However, a more realistic scenario is
obtained from the interpolation between these two cases, considering a certain
number of stochastic contacts per unit time. Here we propose a discrete-time
formulation of the problem of contact-based epidemic spreading. We resolve a
family of models, parameterized by the number of stochastic contact trials per
unit time, that range from the CP to the RP. In contrast to the common
heterogeneous mean-field approach, we focus on the probability of infection of
individual nodes. Using this formulation, we can construct the whole phase
diagram of the different infection models and determine their critical
properties.Comment: 6 pages, 4 figures. Europhys Lett (in press 2010
Editorial: At the Crossroads: Lessons and Challenges in Computational Social Science
The interest of physicists in economic and social questions is not new: during the last decades, we have witnessed the emergence of what is formally called nowadays sociophysics [1] and econophysics [2] that can be grouped into the common term “Interdisciplinary Physics” along with biophysics, medical physics, agrophysics, etc. With tools borrowed from statistical physics and complexity science, among others, these areas of study have already made important contributions to our understanding of how humans organize and interact in our modern society. Large scale data analyses, agent-based modeling and numerical simulations, and finally mathematical modeling, have led to the discovery of new (universal) patterns and their quantitative description in socio-economic systems..
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Individual nodes contribution to the mesoscale of complex networks
The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains
Crowdsourcing Dialect Characterization through Twitter
We perform a large-scale analysis of language diatopic variation using
geotagged microblogging datasets. By collecting all Twitter messages written in
Spanish over more than two years, we build a corpus from which a carefully
selected list of concepts allows us to characterize Spanish varieties on a
global scale. A cluster analysis proves the existence of well defined
macroregions sharing common lexical properties. Remarkably enough, we find that
Spanish language is split into two superdialects, namely, an urban speech used
across major American and Spanish citites and a diverse form that encompasses
rural areas and small towns. The latter can be further clustered into smaller
varieties with a stronger regional character.Comment: 10 pages, 5 figure
Optimal map of the modular structure of complex networks
Modular structure is pervasive in many complex networks of interactions
observed in natural, social and technological sciences. Its study sheds light
on the relation between the structure and function of complex systems.
Generally speaking, modules are islands of highly connected nodes separated by
a relatively small number of links. Every module can have contributions of
links from any node in the network. The challenge is to disentangle these
contributions to understand how the modular structure is built. The main
problem is that the analysis of a certain partition into modules involves, in
principle, as many data as number of modules times number of nodes. To confront
this challenge, here we first define the contribution matrix, the mathematical
object containing all the information about the partition of interest, and
after, we use a Truncated Singular Value Decomposition to extract the best
representation of this matrix in a plane. The analysis of this projection allow
us to scrutinize the skeleton of the modular structure, revealing the structure
of individual modules and their interrelations.Comment: 21 pages, 10 figure
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