3,039 research outputs found
The rich-club phenomenon across complex network hierarchies
The so-called rich-club phenomenon in a complex network is characterized when
nodes of higher degree (hubs) are better connected among themselves than are
nodes with smaller degree. The presence of the rich-club phenomenon may be an
indicator of several interesting high-level network properties, such as
tolerance to hub failures. Here we investigate the existence of the rich-club
phenomenon across the hierarchical degrees of a number of real-world networks.
Our simulations reveal that the phenomenon may appear in some hierarchies but
not in others and, moreover, that it may appear and disappear as we move across
hierarchies. This reveals the interesting possibility of non-monotonic behavior
of the phenomenon; the possible implications of our findings are discussed.Comment: 4 page
Topology and Dynamics in Complex Networks: The Role of Edge Reciprocity
A key issue in complex systems regards the relationship between topology and
dynamics. In this work, we use a recently introduced network property known as
steering coefficient as a means to approach this issue with respect to
different directed complex network systems under varying dynamics. Theoretical
and real-world networks are considered, and the influences of reciprocity and
average degree on the steering coefficient are quantified. A number of
interesting results are reported that can assist the design of complex systems
exhibiting larger or smaller relationships between topology and dynamics
The complex channel networks of bone structure
Bone structure in mammals involves a complex network of channels (Havers and
Volkmann channels) required to nourish the bone marrow cells. This work
describes how three-dimensional reconstructions of such systems can be obtained
and represented in terms of complex networks. Three important findings are
reported: (i) the fact that the channel branching density resembles a power law
implies the existence of distribution hubs; (ii) the conditional node degree
density indicates a clear tendency of connection between nodes with degrees 2
and 4; and (iii) the application of the recently introduced concept of
hierarchical clustering coefficient allows the identification of typical scales
of channel redistribution. A series of important biological insights is drawn
and discussedComment: 3 pages, 1 figure, The following article has been submitted to
Applied Physics Letters. If it is published, it will be found online at
http://apl.aip.org
A Fast and Accurate Nonlinear Spectral Method for Image Recognition and Registration
This article addresses the problem of two- and higher dimensional pattern
matching, i.e. the identification of instances of a template within a larger
signal space, which is a form of registration. Unlike traditional correlation,
we aim at obtaining more selective matchings by considering more strict
comparisons of gray-level intensity. In order to achieve fast matching, a
nonlinear thresholded version of the fast Fourier transform is applied to a
gray-level decomposition of the original 2D image. The potential of the method
is substantiated with respect to real data involving the selective
identification of neuronal cell bodies in gray-level images.Comment: 4 pages, 3 figure
Identifying the starting point of a spreading process in complex networks
When dealing with the dissemination of epidemics, one important question that
can be asked is the location where the contamination began. In this paper, we
analyze three spreading schemes and propose and validate an effective
methodology for the identification of the source nodes. The method is based on
the calculation of the centrality of the nodes on the sampled network,
expressed here by degree, betweenness, closeness and eigenvector centrality. We
show that the source node tends to have the highest measurement values. The
potential of the methodology is illustrated with respect to three theoretical
complex network models as well as a real-world network, the email network of
the University Rovira i Virgili
The Spread of Opinions and Proportional Voting
Election results are determined by numerous social factors that affect the
formation of opinion of the voters, including the network of interactions
between them and the dynamics of opinion influence. In this work we study the
result of proportional elections using an opinion dynamics model similar to
simple opinion spreading over a complex network. Erdos-Renyi, Barabasi-Albert,
regular lattices and randomly augmented lattices are considered as models of
the underlying social networks. The model reproduces the power law behavior of
number of candidates with a given number of votes found in real elections with
the correct slope, a cutoff for larger number of votes and a plateau for small
number of votes. It is found that the small world property of the underlying
network is fundamental for the emergence of the power law regime.Comment: 10 pages, 7 figure
Analyzing Trails in Complex Networks
Even more interesting than the intricate organization of complex networks are
the dynamical behavior of systems which such structures underly. Among the many
types of dynamics, one particularly interesting category involves the evolution
of trails left by moving agents progressing through random walks and dilating
processes in a complex network. The emergence of trails is present in many
dynamical process, such as pedestrian traffic, information flow and metabolic
pathways. Important problems related with trails include the reconstruction of
the trail and the identification of its source, when complete knowledge of the
trail is missing. In addition, the following of trails in multi-agent systems
represent a particularly interesting situation related to pedestrian dynamics
and swarming intelligence. The present work addresses these three issues while
taking into account permanent and transient marks left in the visited nodes.
Different topologies are considered for trail reconstruction and trail source
identification, including four complex networks models and four real networks,
namely the Internet, the US airlines network, an email network and the
scientific collaboration network of complex network researchers. Our results
show that the topology of the network influence in trail reconstruction, source
identification and agent dynamics.Comment: 10 pages, 16 figures. A working manuscript, comments and criticisms
welcome
Prominent effect of soil network heterogeneity on microbial invasion
Using a network representation for real soil samples and mathematical models for microbial spread, we show that the structural heterogeneity of the soil habitat may have a very significant influence on the size of microbial invasions of the soil pore space. In particular, neglecting the soil structural heterogeneity may lead to a substantial underestimation of microbial invasion. Such effects are explained in terms of a crucial interplay between heterogeneity in microbial spread and heterogeneity in the topology of soil networks. The main influence of network topology on invasion is linked to the existence of long channels in soil networks that may act as bridges for transmission of microorganisms between distant parts of soil
Complexity and anisotropy in host morphology make populations safer against epidemic outbreaks
One of the challenges in epidemiology is to account for the complex
morphological structure of hosts such as plant roots, crop fields, farms,
cells, animal habitats and social networks, when the transmission of infection
occurs between contiguous hosts. Morphological complexity brings an inherent
heterogeneity in populations and affects the dynamics of pathogen spread in
such systems. We have analysed the influence of realistically complex host
morphology on the threshold for invasion and epidemic outbreak in an SIR
(susceptible-infected-recovered) epidemiological model. We show that disorder
expressed in the host morphology and anisotropy reduces the probability of
epidemic outbreak and thus makes the system more resistant to epidemic
outbreaks. We obtain general analytical estimates for minimally safe bounds for
an invasion threshold and then illustrate their validity by considering an
example of host data for branching hosts (salamander retinal ganglion cells).
Several spatial arrangements of hosts with different degrees of heterogeneity
have been considered in order to analyse separately the role of shape
complexity and anisotropy in the host population. The estimates for invasion
threshold are linked to morphological characteristics of the hosts that can be
used for determining the threshold for invasion in practical applications.Comment: 21 pages, 8 figure
A Complex Network Approach to Topographical Connections
The neuronal networks in the mammals cortex are characterized by the
coexistence of hierarchy, modularity, short and long range interactions,
spatial correlations, and topographical connections. Particularly interesting,
the latter type of organization implies special demands on the evolutionary and
ontogenetic systems in order to achieve precise maps preserving spatial
adjacencies, even at the expense of isometry. Although object of intensive
biological research, the elucidation of the main anatomic-functional purposes
of the ubiquitous topographical connections in the mammals brain remains an
elusive issue. The present work reports on how recent results from complex
network formalism can be used to quantify and model the effect of topographical
connections between neuronal cells over a number of relevant network properties
such as connectivity, adjacency, and information broadcasting. While the
topographical mapping between two cortical modules are achieved by connecting
nearest cells from each module, three kinds of network models are adopted for
implementing intracortical connections (ICC), including random,
preferential-attachment, and short-range networks. It is shown that, though
spatially uniform and simple, topographical connections between modules can
lead to major changes in the network properties, fostering more effective
intercommunication between the involved neuronal cells and modules. The
possible implications of such effects on cortical operation are discussed.Comment: 5 pages, 5 figure
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