197 research outputs found
Distance-generalized Core Decomposition
The -core of a graph is defined as the maximal subgraph in which every
vertex is connected to at least other vertices within that subgraph. In
this work we introduce a distance-based generalization of the notion of
-core, which we refer to as the -core, i.e., the maximal subgraph in
which every vertex has at least other vertices at distance within
that subgraph. We study the properties of the -core showing that it
preserves many of the nice features of the classic core decomposition (e.g.,
its connection with the notion of distance-generalized chromatic number) and it
preserves its usefulness to speed-up or approximate distance-generalized
notions of dense structures, such as -club.
Computing the distance-generalized core decomposition over large networks is
intrinsically complex. However, by exploiting clever upper and lower bounds we
can partition the computation in a set of totally independent subcomputations,
opening the door to top-down exploration and to multithreading, and thus
achieving an efficient algorithm
Multiwave Mixing in Complex Molecular Media and Dynamic Modes of Light-Wave Transformation
The mechanism of multiwave mixing realized in solutions of complex organic compounds (dyes), in
conditions when higher-order nonlinearities are exhibited along with the cubic one, has been analyzed
theoretically and tested experimentally. Nonlinear recording of dynamic holograms enables signifcant
improvement of the potentialities of holographic methods used for image conversion due to switching
from phase conjugation to smoothing and amplification of the wavefront spatial structure. It has been
demonstrated that a nonstationary energy exchange between the interacting waves is the cause for
transition from the optical bistability mode to the mode of intensity self-oscillations
The evolution of interdisciplinarity in physics research
Science, being a social enterprise, is subject to fragmentation into groups
that focus on specialized areas or topics. Often new advances occur through
cross-fertilization of ideas between sub-fields that otherwise have little
overlap as they study dissimilar phenomena using different techniques. Thus to
explore the nature and dynamics of scientific progress one needs to consider
the large-scale organization and interactions between different subject areas.
Here, we study the relationships between the sub-fields of Physics using the
Physics and Astronomy Classification Scheme (PACS) codes employed for
self-categorization of articles published over the past 25 years (1985-2009).
We observe a clear trend towards increasing interactions between the different
sub-fields. The network of sub-fields also exhibits core-periphery
organization, the nucleus being dominated by Condensed Matter and General
Physics. However, over time Interdisciplinary Physics is steadily increasing
its share in the network core, reflecting a shift in the overall trend of
Physics research.Comment: Published version, 10 pages, 8 figures + Supplementary Informatio
Worldwide spreading of economic crisis
We model the spreading of a crisis by constructing a global economic network
and applying the Susceptible-Infected-Recovered (SIR) epidemic model with a
variable probability of infection. The probability of infection depends on the
strength of economic relations between the pair of countries, and the strength
of the target country. It is expected that a crisis which originates in a large
country, such as the USA, has the potential to spread globally, like the recent
crisis. Surprisingly we show that also countries with much lower GDP, such as
Belgium, are able to initiate a global crisis. Using the {\it k}-shell
decomposition method to quantify the spreading power (of a node), we obtain a
measure of ``centrality'' as a spreader of each country in the economic
network. We thus rank the different countries according to the shell they
belong to, and find the 12 most central countries. These countries are the most
likely to spread a crisis globally. Of these 12 only six are large economies,
while the other six are medium/small ones, a result that could not have been
otherwise anticipated. Furthermore, we use our model to predict the crisis
spreading potential of countries belonging to different shells according to the
crisis magnitude.Comment: 13 pages, 4 figures and Supplementary Materia
Hyperbolic Geometry of Complex Networks
We develop a geometric framework to study the structure and function of
complex networks. We assume that hyperbolic geometry underlies these networks,
and we show that with this assumption, heterogeneous degree distributions and
strong clustering in complex networks emerge naturally as simple reflections of
the negative curvature and metric property of the underlying hyperbolic
geometry. Conversely, we show that if a network has some metric structure, and
if the network degree distribution is heterogeneous, then the network has an
effective hyperbolic geometry underneath. We then establish a mapping between
our geometric framework and statistical mechanics of complex networks. This
mapping interprets edges in a network as non-interacting fermions whose
energies are hyperbolic distances between nodes, while the auxiliary fields
coupled to edges are linear functions of these energies or distances. The
geometric network ensemble subsumes the standard configuration model and
classical random graphs as two limiting cases with degenerate geometric
structures. Finally, we show that targeted transport processes without global
topology knowledge, made possible by our geometric framework, are maximally
efficient, according to all efficiency measures, in networks with strongest
heterogeneity and clustering, and that this efficiency is remarkably robust
with respect to even catastrophic disturbances and damages to the network
structure
Seeds Buffering for Information Spreading Processes
Seeding strategies for influence maximization in social networks have been
studied for more than a decade. They have mainly relied on the activation of
all resources (seeds) simultaneously in the beginning; yet, it has been shown
that sequential seeding strategies are commonly better. This research focuses
on studying sequential seeding with buffering, which is an extension to basic
sequential seeding concept. The proposed method avoids choosing nodes that will
be activated through the natural diffusion process, which is leading to better
use of the budget for activating seed nodes in the social influence process.
This approach was compared with sequential seeding without buffering and single
stage seeding. The results on both real and artificial social networks confirm
that the buffer-based consecutive seeding is a good trade-off between the final
coverage and the time to reach it. It performs significantly better than its
rivals for a fixed budget. The gain is obtained by dynamic rankings and the
ability to detect network areas with nodes that are not yet activated and have
high potential of activating their neighbours.Comment: Jankowski, J., Br\'odka, P., Michalski, R., & Kazienko, P. (2017,
September). Seeds Buffering for Information Spreading Processes. In
International Conference on Social Informatics (pp. 628-641). Springe
Partisan Asymmetries in Online Political Activity
We examine partisan differences in the behavior, communication patterns and
social interactions of more than 18,000 politically-active Twitter users to
produce evidence that points to changing levels of partisan engagement with the
American online political landscape. Analysis of a network defined by the
communication activity of these users in proximity to the 2010 midterm
congressional elections reveals a highly segregated, well clustered partisan
community structure. Using cluster membership as a high-fidelity (87% accuracy)
proxy for political affiliation, we characterize a wide range of differences in
the behavior, communication and social connectivity of left- and right-leaning
Twitter users. We find that in contrast to the online political dynamics of the
2008 campaign, right-leaning Twitter users exhibit greater levels of political
activity, a more tightly interconnected social structure, and a communication
network topology that facilitates the rapid and broad dissemination of
political information.Comment: 17 pages, 10 figures, 6 table
Emergence of influential spreaders in modified rumor models
The burst in the use of online social networks over the last decade has
provided evidence that current rumor spreading models miss some fundamental
ingredients in order to reproduce how information is disseminated. In
particular, recent literature has revealed that these models fail to reproduce
the fact that some nodes in a network have an influential role when it comes to
spread a piece of information. In this work, we introduce two mechanisms with
the aim of filling the gap between theoretical and experimental results. The
first model introduces the assumption that spreaders are not always active
whereas the second model considers the possibility that an ignorant is not
interested in spreading the rumor. In both cases, results from numerical
simulations show a higher adhesion to real data than classical rumor spreading
models. Our results shed some light on the mechanisms underlying the spreading
of information and ideas in large social systems and pave the way for more
realistic diffusion models.Comment: 14 Pages, 6 figures, accepted for publication in Journal of
Statistical Physic
Transition from fractal to non-fractal scalings in growing scale-free networks
Real networks can be classified into two categories: fractal networks and
non-fractal networks. Here we introduce a unifying model for the two types of
networks. Our model network is governed by a parameter . We obtain the
topological properties of the network including the degree distribution,
average path length, diameter, fractal dimensions, and betweenness centrality
distribution, which are controlled by parameter . Interestingly, we show
that by adjusting , the networks undergo a transition from fractal to
non-fractal scalings, and exhibit a crossover from `large' to small worlds at
the same time. Our research may shed some light on understanding the evolution
and relationships of fractal and non-fractal networks.Comment: 7 pages, 3 figures, definitive version accepted for publication in
EPJ
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