67 research outputs found
Phase transition of the susceptible-infected-susceptible dynamics on time-varying configuration model networks
We present a degree-based theoretical framework to study the
susceptible-infected-susceptible (SIS) dynamics on time-varying (rewired)
configuration model networks. Using this framework, we provide a detailed
analysis of the stationary state that covers, for a given structure, every
dynamic regimes easily tuned by the rewiring rate. This analysis is suitable
for the characterization of the phase transition and leads to three main
contributions. (i) We obtain a self-consistent expression for the
absorbing-state threshold, able to capture both collective and hub activation.
(ii) We recover the predictions of a number of existing approaches as limiting
cases of our analysis, providing thereby a unifying point of view for the SIS
dynamics on random networks. (iii) We reinterpret the concept of hub-dominated
phase transition. Within our framework, it appears as a heterogeneous critical
phenomenon : observables for different degree classes have a different scaling
with the infection rate. This leads to the successive activation of the degree
classes beyond the epidemic threshold.Comment: 14 pages, 11 figure
Universality of the stochastic block model
Mesoscopic pattern extraction (MPE) is the problem of finding a partition of
the nodes of a complex network that maximizes some objective function. Many
well-known network inference problems fall in this category, including, for
instance, community detection, core-periphery identification, and imperfect
graph coloring. In this paper, we show that the most popular algorithms
designed to solve MPE problems can in fact be understood as special cases of
the maximum likelihood formulation of the stochastic block model (SBM), or one
of its direct generalizations. These equivalence relations show that the SBM is
nearly universal with respect to MPE problems.Comment: 13 pages, 4 figure
Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm
Efficient stochastic simulation algorithms are of paramount importance to the
study of spreading phenomena on complex networks. Using insights and analytical
results from network science, we discuss how the structure of contacts affects
the efficiency of current algorithms. We show that algorithms believed to
require or even operations per
update---where is the number of nodes---display instead a polynomial
scaling for networks that are either dense or sparse and heterogeneous. This
significantly affects the required computation time for simulations on large
networks. To circumvent the issue, we propose a node-based method combined with
a composition and rejection algorithm, a sampling scheme that has an
average-case complexity of per update for general
networks. This systematic approach is first set-up for Markovian dynamics, but
can also be adapted to a number of non-Markovian processes and can enhance
considerably the study of a wide range of dynamics on networks.Comment: 12 pages, 7 figure
Geometric evolution of complex networks with degree correlations
We present a general class of geometric network growth mechanisms by homogeneous attachment in which the
links created at a given time t are distributed homogeneously between a new node and the existing nodes selected
uniformly. This is achieved by creating links between nodes uniformly distributed in a homogeneous metric
space according to a Fermi-Dirac connection probability with inverse temperature β and general time-dependent
chemical potential μ(t). The chemical potential limits the spatial extent of newly created links. Using a hidden
variable framework, we obtain an analytical expression for the degree sequence and show that μ(t) can be fixed
to yield any given degree distributions, including a scale-free degree distribution. Additionally, we find that
depending on the order in which nodes appear in the network—its history—the degree-degree correlations can
be tuned to be assortative or disassortative. The effect of the geometry on the structure is investigated through the
average clustering coefficient ⟨c⟩. In the thermodynamic limit, we identify a phase transition between a random
regime where ⟨c⟩→ 0 when β 0 when β>βc
Social confinement and mesoscopic localization of epidemics on networks
Recommendations around epidemics tend to focus on individual behaviors, with much less efforts attempting to guide event cancellations and other collective behaviors since most models lack the higher-order structure necessary to describe large gatherings. Through a higher-order description of contagions on networks, we model the impact of a blanket cancellation of events larger than a critical size and find that epidemics can suddenly collapse when interventions operate over groups of individuals rather than at the level of individuals. We relate this phenomenon to the onset of mesoscopic localization, where contagions concentrate around dominant groups
Master equation analysis of mesoscopic localization in contagion dynamics on higher-order networks
Simple models of infectious diseases tend to assume random mixing of
individuals, but real interactions are not random pairwise encounters: they
occur within various types of gatherings such as workplaces, households,
schools, and concerts, best described by a higher-order network structure. We
model contagions on higher-order networks using group-based approximate master
equations, in which we track all states and interactions within a group of
nodes and assume a mean-field coupling between them. Using the
Susceptible-Infected-Susceptible dynamics, our approach reveals the existence
of a mesoscopic localization regime, where a disease can concentrate and
self-sustain only around large groups in the network overall organization. In
this regime, the phase transition is smeared, characterized by an inhomogeneous
activation of the groups. At the mesoscopic level, we observe that the
distribution of infected nodes within groups of a same size can be very
dispersed, even bimodal. When considering heterogeneous networks, both at the
level of nodes and groups, we characterize analytically the region associated
with mesoscopic localization in the structural parameter space. We put in
perspective this phenomenon with eigenvector localization and discuss how a
focus on higher-order structures is needed to discern the more subtle
localization at the mesoscopic level. Finally, we discuss how mesoscopic
localization affects the response to structural interventions and how this
framework could provide important insights for a broad range of dynamics.Comment: 15 pages, 10 figure
Social confinement and mesoscopic localization of epidemics on networks
Recommendations around epidemics tend to focus on individual behaviors, with
much less efforts attempting to guide event cancellations and other collective
behaviors since most models lack the higher-order structure necessary to
describe large gatherings. Through a higher-order description of contagions on
networks, we model the impact of a blanket cancellation of events larger than a
critical size and find that epidemics can suddenly collapse when interventions
operate over groups of individuals rather than at the level of individuals. We
relate this phenomenon to the onset of mesoscopic localization, where
contagions concentrate around dominant groups.Comment: 5 pages, 4 figure
Monitoring of Manufacturing Changes and Formulation Excipients on Solid Oral Dosage Forms of Furosemide Using Chemometrics and Laser-Induced Breakdown Spectroscopy (LIBS)
Peer reviewed: NoNRC publication: Ye
Annual Research Review: Sleep problems in childhood psychiatric disorders – a review of the latest science
Background
Hippocrates flagged the value of sleep for good health. Nonetheless, historically, researchers with an interest in developmental psychopathology have largely ignored a possible role for atypical sleep. Recently, however, there has been a surge of interest in this area, perhaps reflecting increased evidence that disturbed or insufficient sleep can result in poor functioning in numerous domains. This review outlines what is known about sleep in the psychiatric diagnoses most relevant to children and for which associations with sleep are beginning to be understood. While based on a comprehensive survey of the literature, the focus of the current review is on the latest science (largely from 2010). There is a description of both concurrent and longitudinal links as well as possible mechanisms underlying associations. Preliminary treatment research is also considered which suggests that treating sleep difficulties may result in improvements in behavioural areas beyond sleep quality.
Findings
To maximise progress in this field, there now needs to be: (a) greater attention to the assessment of sleep in children; (b) sleep research on a wider range of psychiatric disorders; (c) a greater focus on and examination of mechanisms underlying associations; (d) a clearer consideration ofdevelopmental questions and (e) large-scale well-designed treatment studies.
Conclusions
While sleep problems may sometimes be missed by parents and healthcare providers; hence constituting a hidden risk for other psychopathologies – knowing about these difficulties creates unique opportunities. The current excitement in this field from experts in diverse areas including developmental psychology, clinical psychology, genetics and neuropsychology should make these opportunities a reality
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