10,203 research outputs found
Self-similarity of complex networks
Complex networks have been studied extensively due to their relevance to many
real systems as diverse as the World-Wide-Web (WWW), the Internet, energy
landscapes, biological and social networks
\cite{ab-review,mendes,vespignani,newman,amaral}. A large number of real
networks are called ``scale-free'' because they show a power-law distribution
of the number of links per node \cite{ab-review,barabasi1999,faloutsos}.
However, it is widely believed that complex networks are not {\it length-scale}
invariant or self-similar. This conclusion originates from the ``small-world''
property of these networks, which implies that the number of nodes increases
exponentially with the ``diameter'' of the network
\cite{erdos,bollobas,milgram,watts}, rather than the power-law relation
expected for a self-similar structure. Nevertheless, here we present a novel
approach to the analysis of such networks, revealing that their structure is
indeed self-similar. This result is achieved by the application of a
renormalization procedure which coarse-grains the system into boxes containing
nodes within a given "size". Concurrently, we identify a power-law relation
between the number of boxes needed to cover the network and the size of the box
defining a finite self-similar exponent. These fundamental properties, which
are shown for the WWW, social, cellular and protein-protein interaction
networks, help to understand the emergence of the scale-free property in
complex networks. They suggest a common self-organization dynamics of diverse
networks at different scales into a critical state and in turn bring together
previously unrelated fields: the statistical physics of complex networks with
renormalization group, fractals and critical phenomena.Comment: 28 pages, 12 figures, more informations at http://www.jamlab.or
The Geography of Scientific Productivity: Scaling in U.S. Computer Science
Here we extract the geographical addresses of authors in the Citeseer
database of computer science papers. We show that the productivity of research
centres in the United States follows a power-law regime, apart from the most
productive centres for which we do not have enough data to reach definite
conclusions. To investigate the spatial distribution of computer science
research centres in the United States, we compute the two-point correlation
function of the spatial point process and show that the observed power-laws do
not disappear even when we change the physical representation from geographical
space to cartogram space. Our work suggests that the effect of physical
location poses a challenge to ongoing efforts to develop realistic models of
scientific productivity. We propose that the introduction of a fine scale
geography may lead to more sophisticated indicators of scientific output.Comment: 6 pages, 3 figures; minor change
The Earth as a living planet: human-type diseases in the earthquake preparation process
The new field of complex systems supports the view that a number of systems
arising from disciplines as diverse as physics, biology, engineering, and
economics may have certain quantitative features that are intriguingly similar.
The earth is a living planet where many complex systems run perfectly without
stopping at all. The earthquake generation is a fundamental sign that the earth
is a living planet. Recently, analyses have shown that human-brain-type disease
appears during the earthquake generation process. Herein, we show that
human-heart-type disease appears during the earthquake preparation of the
earthquake process. The investigation is mainly attempted by means of critical
phenomena, which have been proposed as the likely paradigm to explain the
origins of both heart electric fluctuations and fracture induced
electromagnetic fluctuations. We show that a time window of the damage
evolution within the heterogeneous Earth's crust and the healthy heart's
electrical action present the characteristic features of the critical point of
a thermal second order phase transition. A dramatic breakdown of critical
characteristics appears in the tail of the fracture process of heterogeneous
system and the injury heart's electrical action. Analyses by means of Hurst
exponent and wavelet decomposition further support the hypothesis that a
dynamical analogy exists between the geological and biological systems under
study
Entropy-scaling search of massive biological data
Many datasets exhibit a well-defined structure that can be exploited to
design faster search tools, but it is not always clear when such acceleration
is possible. Here, we introduce a framework for similarity search based on
characterizing a dataset's entropy and fractal dimension. We prove that
searching scales in time with metric entropy (number of covering hyperspheres),
if the fractal dimension of the dataset is low, and scales in space with the
sum of metric entropy and information-theoretic entropy (randomness of the
data). Using these ideas, we present accelerated versions of standard tools,
with no loss in specificity and little loss in sensitivity, for use in three
domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics
(MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search
(esFragBag, 10x speedup of FragBag). Our framework can be used to achieve
"compressive omics," and the general theory can be readily applied to data
science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo
Measuring the dimension of partially embedded networks
Scaling phenomena have been intensively studied during the past decade in the
context of complex networks. As part of these works, recently novel methods
have appeared to measure the dimension of abstract and spatially embedded
networks. In this paper we propose a new dimension measurement method for
networks, which does not require global knowledge on the embedding of the
nodes, instead it exploits link-wise information (link lengths, link delays or
other physical quantities). Our method can be regarded as a generalization of
the spectral dimension, that grasps the network's large-scale structure through
local observations made by a random walker while traversing the links. We apply
the presented method to synthetic and real-world networks, including road maps,
the Internet infrastructure and the Gowalla geosocial network. We analyze the
theoretically and empirically designated case when the length distribution of
the links has the form P(r) ~ 1/r. We show that while previous dimension
concepts are not applicable in this case, the new dimension measure still
exhibits scaling with two distinct scaling regimes. Our observations suggest
that the link length distribution is not sufficient in itself to entirely
control the dimensionality of complex networks, and we show that the proposed
measure provides information that complements other known measures
Map online system using internet-based image catalogue
Digital maps carry along its geodata information such as coordinate that is important in one particular topographic and thematic map. These geodatas are meaningful especially in military field. Since the maps carry along this information, its makes the size of the images is too big. The bigger size, the bigger storage is required to allocate the image file. It also can cause longer loading time. These conditions make it did not suitable to be applied in image catalogue approach via internet environment. With compression techniques, the image size can be reduced and the quality of the image is still guaranteed without much changes. This report is paying attention to one of the image compression technique using wavelet technology. Wavelet technology is much batter than any other image compression technique nowadays. As a result, the compressed images applied to a system called Map Online that used Internet-based Image Catalogue approach. This system allowed user to buy map online. User also can download the maps that had been bought besides using the searching the map. Map searching is based on several meaningful keywords. As a result, this system is expected to be used by Jabatan Ukur dan Pemetaan Malaysia (JUPEM) in order to make the organization vision is implemented
Characterization of complex networks: A survey of measurements
Each complex network (or class of networks) presents specific topological
features which characterize its connectivity and highly influence the dynamics
of processes executed on the network. The analysis, discrimination, and
synthesis of complex networks therefore rely on the use of measurements capable
of expressing the most relevant topological features. This article presents a
survey of such measurements. It includes general considerations about complex
network characterization, a brief review of the principal models, and the
presentation of the main existing measurements. Important related issues
covered in this work comprise the representation of the evolution of complex
networks in terms of trajectories in several measurement spaces, the analysis
of the correlations between some of the most traditional measurements,
perturbation analysis, as well as the use of multivariate statistics for
feature selection and network classification. Depending on the network and the
analysis task one has in mind, a specific set of features may be chosen. It is
hoped that the present survey will help the proper application and
interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of
measurements for inclusion are welcomed by the author
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