1,247 research outputs found
Evaluating Local Community Methods in Networks
We present a new benchmarking procedure that is unambiguous and specific to
local community-finding methods, allowing one to compare the accuracy of
various methods. We apply this to new and existing algorithms. A simple class
of synthetic benchmark networks is also developed, capable of testing
properties specific to these local methods.Comment: 8 pages, 9 figures, code included with sourc
Prospectives
Tiré de: Prospectives, vol. 24, no 2, avril 1988.Titre de l'écran-titre (visionné le 24 janv. 2013
Topological Speed Limits to Network Synchronization
We study collective synchronization of pulse-coupled oscillators interacting
on asymmetric random networks. We demonstrate that random matrix theory can be
used to accurately predict the speed of synchronization in such networks in
dependence on the dynamical and network parameters. Furthermore, we show that
the speed of synchronization is limited by the network connectivity and stays
finite, even if the coupling strength becomes infinite. In addition, our
results indicate that synchrony is robust under structural perturbations of the
network dynamics.Comment: 5 pages, 3 figure
Breaking Synchrony by Heterogeneity in Complex Networks
For networks of pulse-coupled oscillators with complex connectivity, we
demonstrate that in the presence of coupling heterogeneity precisely timed
periodic firing patterns replace the state of global synchrony that exists in
homogenous networks only. With increasing disorder, these patterns persist
until they reach a critical temporal extent that is of the order of the
interaction delay. For stronger disorder these patterns cease to exist and only
asynchronous, aperiodic states are observed. We derive self-consistency
equations to predict the precise temporal structure of a pattern from the
network heterogeneity. Moreover, we show how to design heterogenous coupling
architectures to create an arbitrary prescribed pattern.Comment: 4 pages, 3 figure
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The interplay of microscopic and mesoscopic structure in complex networks
Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks
Estimation of metabolite networks with regard to a specific covariable: applications to plant and human data
In systems biology, where a main goal is acquiring knowledge of biological systems, one of the challenges is inferring biochemical interactions from different molecular entities such as metabolites. In this area, the metabolome possesses a unique place for reflecting “true exposure” by being sensitive to variation coming from genetics, time, and environmental stimuli. While influenced by many different reactions, often the research interest needs to be focused on variation coming from a certain source, i.e. a certain covariable Xm . Objective Here, we use network analysis methods to recover a set of metabolite relationships, by finding metabolites sharing a similar relation to Xm . Metabolite values are based on information coming from individuals’ Xm status which might interact with other covariables. Methods Alternative to using the original metabolite values, the total information is decomposed by utilizing a linear regression model and the part relevant to Xm is further used. For two datasets, two different network estimation methods are considered. The first is weighted gene co-expression network analysis based on correlation coefficients. The second method is graphical LASSO based on partial correlations. Results We observed that when using the parts related to the specific covariable of interest, resulting estimated networks display higher interconnectedness. Additionally, several groups of biologically associated metabolites (very large density lipoproteins, lipoproteins, etc.) were identified in the human data example. Conclusions This work demonstrates how information on the study design can be incorporated to estimate metabolite networks. As a result, sets of interconnected metabolites can be clustered together with respect to their relation to a covariable of interest
A Research Agenda to Underpin Malaria Eradication
Pedro Alonso and colleagues introduce the Malaria Eradication Research Agenda (malERA) initiative and the set of articles published in this PLoS Medicine Supplement that distill the research questions key to malaria eradication
Impact of stratospheric air and surface emissions on tropospheric nitrous oxide during ATom
We measured the global distribution of tropospheric N2O mixing ratios during the NASA airborne Atmospheric Tomography (ATom) mission. ATom measured concentrations of ∼ 300 gas species and aerosol properties in 647 vertical profiles spanning the Pacific, Atlantic, Arctic, and much of the Southern Ocean basins, nearly from pole to pole, over four seasons (2016–2018). We measured N2O concentrations at 1 Hz using a quantum cascade laser spectrometer (QCLS). We introduced a new spectral retrieval method to account for the pressure and temperature sensitivity of the instrument when deployed on aircraft. This retrieval strategy improved the precision of our ATom QCLS N2O measurements by a factor of three (based on the standard deviation of calibration measurements). Our measurements show that most of the variance of N2O mixing ratios in the troposphere is driven by the influence of N2O-depleted stratospheric air, especially at mid- and high latitudes. We observe the downward propagation of lower N2O mixing ratios (compared to surface stations) that tracks the influence of stratosphere–troposphere exchange through the tropospheric column down to the surface. The highest N2O mixing ratios occur close to the Equator, extending through the boundary layer and free troposphere. We observed influences from a complex and diverse mixture of N2O sources, with emission source types identified using the rich suite of chemical species measured on ATom and the geographical origin calculated using an atmospheric transport model. Although ATom flights were mostly over the oceans, the most prominent N2O enhancements were associated with anthropogenic emissions, including from industry (e.g., oil and gas), urban sources, and biomass burning, especially in the tropical Atlantic outflow from Africa. Enhanced N2O mixing ratios are mostly associated with pollution-related tracers arriving from the coastal area of Nigeria. Peaks of N2O are often associated with indicators of photochemical processing, suggesting possible unexpected source processes. In most cases, the results show how difficult it is to separate the mixture of different sources in the atmosphere, which may contribute to uncertainties in the N2O global budget. The extensive data set from ATom will help improve the understanding of N2O emission processes and their representation in global models.This research has been supported by the National Aeronautics and Space Administration (grant nos. NNX15AJ23G, NNX17AF54G, NNX15AG58A, NNX15AH33A, and 80NSSC19K0124) and the National Science Foundation (grant nos. 1852977, AGS-1547626, and AGS-1623745)
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