284 research outputs found
Distributed Community Detection in Dynamic Graphs
Inspired by the increasing interest in self-organizing social opportunistic
networks, we investigate the problem of distributed detection of unknown
communities in dynamic random graphs. As a formal framework, we consider the
dynamic version of the well-studied \emph{Planted Bisection Model}
\sdG(n,p,q) where the node set of the network is partitioned into two
unknown communities and, at every time step, each possible edge is
active with probability if both nodes belong to the same community, while
it is active with probability (with ) otherwise. We also consider a
time-Markovian generalization of this model.
We propose a distributed protocol based on the popular \emph{Label
Propagation Algorithm} and prove that, when the ratio is larger than
(for an arbitrarily small constant ), the protocol finds the right
"planted" partition in time even when the snapshots of the dynamic
graph are sparse and disconnected (i.e. in the case ).Comment: Version I
Phylogenetic Analysis of Kindlins Suggests Subfunctionalization of an Ancestral Unduplicated Kindlin into Three Paralogs in Vertebrates
Kindlin proteins represent a newly discovered family of evolutionarily conserved FERM domain-containing proteins. This family includes three highly conserved proteins: Kindlin-1, Kindlin-2 and Kindlin-3. All three Kindlin proteins are associated with focal adhesions and are involved in integrin activation. The FERM domain of each Kindlin is bipartite and plays a key role in integrin activation. We herein explore for the first time the evolutionary history of these proteins. The phylogeny of the Kindlins suggests a single ancestral Kindlin protein present in even the earliest metazoan ie, hydra. This protein then underwent duplication events in insects and also experienced genome duplication in vertebrates, leading to the Kindlin family. A comparative study of the Kindlin paralogs showed that Kindlin-2 is the slowest evolving protein among the three family members. The analysis of synonymous and non-synonymous substitutions in orthologous Kindlin sequences in different species showed that all three Kindlins have been evolving under the influence of purifying selection. The expression pattern of Kindlins along with phylogenetic studies supports the subfunctionalization model of gene duplication
Differential item functioning due to gender between depression and anxiety items among Chilean adolescents
Although much is known about the higher prevalence of anxiety and depressive disorders among adolescent females, less is known about the differential item endorsement due to gender in items of commonly scales used to measure anxiety and depression. We conducted a study to examine if adolescent males and females from Chile differed on how they endorsed the items of the Youth Self-Report (YSR) anxious/depressed problem scale. We used data from a cross-sectional sample consisting of 925 participants (Mean age = 14, SD=1.3, 49% females) of low to lower-middle socioeconomic status. A two-parameter logistic (2PL) IRT DIF model was fit. Results revealed differential item endorsement (DIF) by gender for six of the 13 items with adolescent females being more likely to endorse a depression item while males were found more likely to endorse anxiety items. Findings suggest that items found in commonly utilized measures of anxiety and depression symptoms may not equally capture true levels of these behavioral problems among adolescent males and females. Given the high levels of mental disorders in Chile and surrounding countries, further attention should be focused on increasing the number of empirical studies examining potential gender differences in the assessment of mental health problems among Latin American populations to better aid our understanding of the phenomenology and determinants of these problems in the region.R01 HD033487 - NICHD NIH HHS; R01 DA021181-05 - NIDA NIH HHS; R01 DA021181 - NIDA NIH HH
Network Transitivity and Matrix Models
This paper is a step towards a systematic theory of the transitivity
(clustering) phenomenon in random networks. A static framework is used, with
adjacency matrix playing the role of the dynamical variable. Hence, our model
is a matrix model, where matrices are random, but their elements take values 0
and 1 only. Confusion present in some papers where earlier attempts to
incorporate transitivity in a similar framework have been made is hopefully
dissipated. Inspired by more conventional matrix models, new analytic
techniques to develop a static model with non-trivial clustering are
introduced. Computer simulations complete the analytic discussion.Comment: 11 pages, 7 eps figures, 2-column revtex format, print bug correcte
Percolation in the classical blockmodel
Classical blockmodel is known as the simplest among models of networks with
community structure. The model can be also seen as an extremely simply example
of interconnected networks. For this reason, it is surprising that the
percolation transition in the classical blockmodel has not been examined so
far, although the phenomenon has been studied in a variety of much more
complicated models of interconnected and multiplex networks. In this paper we
derive the self-consistent equation for the size the global percolation cluster
in the classical blockmodel. We also find the condition for percolation
threshold which characterizes the emergence of the giant component. We show
that the discussed percolation phenomenon may cause unexpected problems in a
simple optimization process of the multilevel network construction. Numerical
simulations confirm the correctness of our theoretical derivations.Comment: 7 pages, 6 figure
Effect of disorder on superconductivity in the boson-fermion model
We study how a randomness of either boson or fermion site energies affects
the superconducting phase of the boson fermion model. We find that, contrary to
what is expected for s-wave superconductors, the non-magnetic disorder is
detrimental to the s-wave superconductivity. However, depending in which
subsystem the disorder is located, we can observe different channels being
affected. Weak disorder of the fermion subsystem is responsible mainly for
renormalization of the single particle density of states while disorder in the
boson subsystem directly leads to fluctuation of the strength of the effective
pairing between fermions.Comment: 7 pages, 6 figures. Physical Review B (accepted for publication
Laser-induced collective excitations in a two-component Fermi gas
We consider the linear density response of a two-component (superfluid) Fermi
gas of atoms when the perturbation is caused by laser light. We show that
various types of laser excitation schemes can be transformed into linear
density perturbations, however, a Bragg spectroscopy scheme is needed for
transferring energy and momentum into a collective mode. This makes other types
of laser probing schemes insensitive for collective excitations and therefore
well suited for the detection of the superfluid order parameter. We show that
for the special case when laser light is coupled between the two components of
the Fermi gas, density response is always absent in a homogeneous system.Comment: 6 pages, no figure
Social Cohesion, Structural Holes, and a Tale of Two Measures
EMBARGOED - author can archive pre-print or post-print on any open access repository after 12 months from publication. Publication date is May 2013 so embargoed until May 2014.This is an author’s accepted manuscript (deposited at arXiv arXiv:1211.0719v2 [physics.soc-ph] ), which was subsequently published in Journal of Statistical Physics May 2013, Volume 151, Issue 3-4, pp 745-764. The final publication is available at link.springer.com http://link.springer.com/article/10.1007/s10955-013-0722-
Subgraphs in random networks
Understanding the subgraph distribution in random networks is important for
modelling complex systems. In classic Erdos networks, which exhibit a
Poissonian degree distribution, the number of appearances of a subgraph G with
n nodes and g edges scales with network size as \mean{G} ~ N^{n-g}. However,
many natural networks have a non-Poissonian degree distribution. Here we
present approximate equations for the average number of subgraphs in an
ensemble of random sparse directed networks, characterized by an arbitrary
degree sequence. We find new scaling rules for the commonly occurring case of
directed scale-free networks, in which the outgoing degree distribution scales
as P(k) ~ k^{-\gamma}. Considering the power exponent of the degree
distribution, \gamma, as a control parameter, we show that random networks
exhibit transitions between three regimes. In each regime the subgraph number
of appearances follows a different scaling law, \mean{G} ~ N^{\alpha}, where
\alpha=n-g+s-1 for \gamma<2, \alpha=n-g+s+1-\gamma for 2<\gamma<\gamma_c, and
\alpha=n-g for \gamma>\gamma_c, s is the maximal outdegree in the subgraph, and
\gamma_c=s+1. We find that certain subgraphs appear much more frequently than
in Erdos networks. These results are in very good agreement with numerical
simulations. This has implications for detecting network motifs, subgraphs that
occur in natural networks significantly more than in their randomized
counterparts.Comment: 8 pages, 5 figure
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