11,552 research outputs found
k-core (bootstrap) percolation on complex networks: Critical phenomena and nonlocal effects
We develop the theory of the k-core (bootstrap) percolation on uncorrelated
random networks with arbitrary degree distributions. We show that the k-core
percolation is an unusual, hybrid phase transition with a jump emergence of the
k-core as at a first order phase transition but also with a critical
singularity as at a continuous transition. We describe the properties of the
k-core, explain the meaning of the order parameter for the k-core percolation,
and reveal the origin of the specific critical phenomena. We demonstrate that a
so-called ``corona'' of the k-core plays a crucial role (corona is a subset of
vertices in the k-core which have exactly k neighbors in the k-core). It turns
out that the k-core percolation threshold is at the same time the percolation
threshold of finite corona clusters. The mean separation of vertices in corona
clusters plays the role of the correlation length and diverges at the critical
point. We show that a random removal of even one vertex from the k-core may
result in the collapse of a vast region of the k-core around the removed
vertex. The mean size of this region diverges at the critical point. We find an
exact mapping of the k-core percolation to a model of cooperative relaxation.
This model undergoes critical relaxation with a divergent rate at some critical
moment.Comment: 11 pages, 8 figure
k-core organization of complex networks
We analytically describe the architecture of randomly damaged uncorrelated
networks as a set of successively enclosed substructures -- k-cores. The k-core
is the largest subgraph where vertices have at least k interconnections. We
find the structure of k-cores, their sizes, and their birth points -- the
bootstrap percolation thresholds. We show that in networks with a finite mean
number z_2 of the second-nearest neighbors, the emergence of a k-core is a
hybrid phase transition. In contrast, if z_2 diverges, the networks contain an
infinite sequence of k-cores which are ultra-robust against random damage.Comment: 5 pages, 3 figure
Controlling the uncontrolled: Are there incidental experimenter effects on physiologic responding?
The degree to which experimenters shape participant behavior has long been of interest in experimental social science research. Here, we extend this question to the domain of peripheral psychophysiology, where experimenters often have direct, physical contact with participants, yet researchers do not consistently test for their influence. We describe analytic tools for examining experimenter effects in peripheral physiology. Using these tools, we investigate nine data sets totaling 1,341 participants and 160 experimenters across different roles (e.g., lead research assistants, evaluators, confederates) to demonstrate how researchers can test for experimenter effects in participant autonomic nervous system activity during baseline recordings and reactivity to study tasks. Our results showed (a) little to no significant variance in participants' physiological reactivity due to their experimenters, and (b) little to no evidence that three characteristics of experimenters that are well known to shape interpersonal interactions-status (using five studies with 682 total participants), gender (using two studies with 359 total participants), and race (in two studies with 554 total participants)-influenced participants' physiology. We highlight several reasons that experimenter effects in physiological data are still cause for concern, including the fact that experimenters in these studies were already restricted on a number of characteristics (e.g., age, education). We present recommendations for examining and reducing experimenter effects in physiological data and discuss implications for replication
Bootstrap Percolation on Complex Networks
We consider bootstrap percolation on uncorrelated complex networks. We obtain
the phase diagram for this process with respect to two parameters: , the
fraction of vertices initially activated, and , the fraction of undamaged
vertices in the graph. We observe two transitions: the giant active component
appears continuously at a first threshold. There may also be a second,
discontinuous, hybrid transition at a higher threshold. Avalanches of
activations increase in size as this second critical point is approached,
finally diverging at this threshold. We describe the existence of a special
critical point at which this second transition first appears. In networks with
degree distributions whose second moment diverges (but whose first moment does
not), we find a qualitatively different behavior. In this case the giant active
component appears for any and , and the discontinuous transition is
absent. This means that the giant active component is robust to damage, and
also is very easily activated. We also formulate a generalized bootstrap
process in which each vertex can have an arbitrary threshold.Comment: 9 pages, 3 figure
The Near Infrared NaI Doublet Feature in M Stars
The NaI near-infrared doublet has been used to indicate the dwarf/giant
population in composite systems, but its interpretation is still a contentious
issue. In order to understand the behaviour of this controversial feature, we
study the observed and synthetic spectra of cool stars. We conclude that the
NaI infrared feature can be used as a dwarf/giant discriminator. We propose a
modified definition of the NaI index by locating the red continuum at 8234
angstrons and by measuring the equivalent width in the range 8172-8197
angstrons, avoiding the region at lambda > 8197 angstrons, which contains VI,
ZrI, FeI and TiO lines. We also study the dependence of this feature on stellar
atmospheric parameters.Comment: 9 pages, (TeX file) + 7 Figures in Postscript format. Accepted for
publication in The Astrophysical Journa
The interplay of university and industry through the FP5 network
To improve the quality of life in a modern society it is essential to reduce
the distance between basic research and applications, whose crucial roles in
shaping today's society prompt us to seek their understanding. Existing studies
on this subject, however, have neglected the network character of the
interaction between university and industry. Here we use state-of-the-art
network theory methods to analyze this interplay in the so-called Framework
Programme--an initiative which sets out the priorities for the European Union's
research and technological development. In particular we study in the 5th
Framework Programme (FP5) the role played by companies and scientific
institutions and how they contribute to enhance the relationship between
research and industry. Our approach provides quantitative evidence that while
firms are size hierarchically organized, universities and research
organizations keep the network from falling into pieces, paving the way for an
effective knowledge transfer.Comment: 21 pages (including Appendix), 8 figures. Published online at
http://stacks.iop.org/1367-2630/9/18
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