5,333 research outputs found
Stability of a two-sublattice spin-glass model
We study the stability of the replica-symmetric solution of a two-sublattice
infinite-range spin-glass model, which can describe the transition from
antiferromagnetic to spin glass state. The eigenvalues associated with
replica-symmetric perturbations are in general complex. The natural
generalization of the usual stability condition is to require the real part of
these eigenvalues to be positive. The necessary and sufficient conditions for
all the roots of the secular equation to have positive real parts is given by
the Hurwitz criterion. The generalized stability condition allows a consistent
analysis of the phase diagram within the replica-symmetric approximation.Comment: 21 pages, 5 figure
Border trees of complex networks
The comprehensive characterization of the structure of complex networks is
essential to understand the dynamical processes which guide their evolution.
The discovery of the scale-free distribution and the small world property of
real networks were fundamental to stimulate more realistic models and to
understand some dynamical processes such as network growth. However, properties
related to the network borders (nodes with degree equal to one), one of its
most fragile parts, remain little investigated and understood. The border nodes
may be involved in the evolution of structures such as geographical networks.
Here we analyze complex networks by looking for border trees, which are defined
as the subgraphs without cycles connected to the remainder of the network
(containing cycles) and terminating into border nodes. In addition to
describing an algorithm for identification of such tree subgraphs, we also
consider a series of their measurements, including their number of vertices,
number of leaves, and depth. We investigate the properties of border trees for
several theoretical models as well as real-world networks.Comment: 5 pages, 1 figure, 2 tables. A working manuscript, comments and
suggestions welcome
Seeking for Simplicity in Complex Networks
Complex networks can be understood as graphs whose connectivity deviates from
those of regular or near-regular graphs, which are understood as being
`simple'. While a great deal of the attention so far dedicated to complex
networks has been duly driven by the `complex' nature of these structures, in
this work we address the identification of simplicity, in the sense of
regularity, in complex networks. The basic idea is to seek for subgraphs
exhibiting small dispersion (e.g. standard deviation or entropy) of local
measurements such as the node degree and clustering coefficient. This approach
paves the way for the identification of subgraphs (patches) with nearly uniform
connectivity, therefore complementing the characterization of the complexity of
networks. We also performed analysis of cascade failures, revealing that the
removal of vertices in `simple' regions results in smaller damage to the
network structure than the removal of vertices in the heterogeneous regions. We
illustrate the potential of the proposed methodology with respect to four
theoretical models as well as protein-protein interaction networks of three
different species. Our results suggest that the simplicity of protein
interaction grows as the result of natural selection. This increase in
simplicity makes these networks more robust to cascade failures.Comment: 5 pages, 3 figures, 1 table. Submitted to Physical Review Letter
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
Complex networks: the key to systems biology
Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.FAPESPCNP
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
A Complex Networks Approach for Data Clustering
Many methods have been developed for data clustering, such as k-means,
expectation maximization and algorithms based on graph theory. In this latter
case, graphs are generally constructed by taking into account the Euclidian
distance as a similarity measure, and partitioned using spectral methods.
However, these methods are not accurate when the clusters are not well
separated. In addition, it is not possible to automatically determine the
number of clusters. These limitations can be overcome by taking into account
network community identification algorithms. In this work, we propose a
methodology for data clustering based on complex networks theory. We compare
different metrics for quantifying the similarity between objects and take into
account three community finding techniques. This approach is applied to two
real-world databases and to two sets of artificially generated data. By
comparing our method with traditional clustering approaches, we verify that the
proximity measures given by the Chebyshev and Manhattan distances are the most
suitable metrics to quantify the similarity between objects. In addition, the
community identification method based on the greedy optimization provides the
smallest misclassification rates.Comment: 9 pages, 8 Figure
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