608 research outputs found
Edge based stochastic block model statistical inference
Community detection in graphs often relies on ad hoc algorithms with no clear
specification about the node partition they define as the best, which leads to
uninterpretable communities. Stochastic block models (SBM) offer a framework to
rigorously define communities, and to detect them using statistical inference
method to distinguish structure from random fluctuations. In this paper, we
introduce an alternative definition of SBM based on edge sampling. We derive
from this definition a quality function to statistically infer the node
partition used to generate a given graph. We then test it on synthetic graphs,
and on the zachary karate club network
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
Lymphatic vessels in human adipose tissue
Despite being considered present in most vascularised tissues, lymphatic vessels have not been properly shown in human adipose tissue (AT). Our goal in this study is to investigate an unanswered question in AT biology, regarding lymphatic network presence in tissue parenchyma. Using human subcutaneous (S-) and visceral (V-) AT samples with whole mount staining for lymphatic specific markers and three-dimensional imaging, we showed lymphatic capillaries and larger lymphatic vessels in the human VAT. Conversely, in the human SAT, microcirculatory lymphatic vascular structures were rarely detected and no initial lymphatics were found
A shadowing problem in the detection of overlapping communities: lifting the resolution limit through a cascading procedure
Community detection is the process of assigning nodes and links in
significant communities (e.g. clusters, function modules) and its development
has led to a better understanding of complex networks. When applied to sizable
networks, we argue that most detection algorithms correctly identify prominent
communities, but fail to do so across multiple scales. As a result, a
significant fraction of the network is left uncharted. We show that this
problem stems from larger or denser communities overshadowing smaller or
sparser ones, and that this effect accounts for most of the undetected
communities and unassigned links. We propose a generic cascading approach to
community detection that circumvents the problem. Using real and artificial
network datasets with three widely used community detection algorithms, we show
how a simple cascading procedure allows for the detection of the missing
communities. This work highlights a new detection limit of community structure,
and we hope that our approach can inspire better community detection
algorithms.Comment: 14 pages, 12 figures + supporting information (5 pages, 6 tables, 3
figures
Phase transitions and memory effects in the dynamics of Boolean networks
The generating functional method is employed to investigate the synchronous
dynamics of Boolean networks, providing an exact result for the system dynamics
via a set of macroscopic order parameters. The topology of the networks studied
and its constituent Boolean functions represent the system's quenched disorder
and are sampled from a given distribution. The framework accommodates a variety
of topologies and Boolean function distributions and can be used to study both
the noisy and noiseless regimes; it enables one to calculate correlation
functions at different times that are inaccessible via commonly used
approximations. It is also used to determine conditions for the annealed
approximation to be valid, explore phases of the system under different levels
of noise and obtain results for models with strong memory effects, where
existing approximations break down. Links between BN and general Boolean
formulas are identified and common results to both system types are
highlighted
Optimal interdependence between networks for the evolution of cooperation
Recent research has identified interactions between networks as crucial for the outcome of evolutionary
games taking place on them. While the consensus is that interdependence does promote cooperation by
means of organizational complexity and enhanced reciprocity that is out of reach on isolated networks, we
here address the question just how much interdependence there should be. Intuitively, one might assume
the more the better. However, we show that in fact only an intermediate density of sufficiently strong
interactions between networks warrants an optimal resolution of social dilemmas. This is due to an intricate
interplay between the heterogeneity that causes an asymmetric strategy flow because of the additional links
between the networks, and the independent formation of cooperative patterns on each individual network.
Presented results are robust to variations of the strategy updating rule, the topology of interdependent
networks, and the governing social dilemma, thus suggesting a high degree of universality
Network-based models for social recommender systems
With the overwhelming online products available in recent years, there is an
increasing need to filter and deliver relevant personalized advice for users.
Recommender systems solve this problem by modeling and predicting individual
preferences for a great variety of items such as movies, books or research
articles. In this chapter, we explore rigorous network-based models that
outperform leading approaches for recommendation. The network models we
consider are based on the explicit assumption that there are groups of
individuals and of items, and that the preferences of an individual for an item
are determined only by their group memberships. The accurate prediction of
individual user preferences over items can be accomplished by different
methodologies, such as Monte Carlo sampling or Expectation-Maximization
methods, the latter resulting in a scalable algorithm which is suitable for
large datasets
The International Postal Network and Other Global Flows as Proxies for National Wellbeing.
The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals.Project LASAGNE Contract No. 318132 (STREP) - funded by the European CommissionThis is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.015597
Marine probiotics: increasing coral resistance to bleaching through microbiome manipulation
Although the early coral reef-bleaching warning system (NOAA/USA) is established, there is no feasible treatment that can minimize temperature bleaching and/or disease impacts on corals in the field. Here, we present the first attempts to extrapolate the widespread and well-established use of bacterial consortia to protect or improve health in other organisms (e.g., humans and plants) to corals. Manipulation of the coral-associated microbiome was facilitated through addition of a consortium of native (isolated from Pocillopora damicornis and surrounding seawater) putatively beneficial microorganisms for corals (pBMCs), including five Pseudoalteromonas sp., a Halomonas taeanensis and a Cobetia marina-related species strains. The results from a controlled aquarium experiment in two temperature regimes (26 °C and 30 °C) and four treatments (pBMC; pBMC with pathogen challenge – Vibrio coralliilyticus, VC; pathogen challenge, VC; and control) revealed the ability of the pBMC consortium to partially mitigate coral bleaching. Significantly reduced coral-bleaching metrics were observed in pBMC-inoculated corals, in contrast to controls without pBMC addition, especially challenged corals, which displayed strong bleaching signs as indicated by significantly lower photopigment contents and Fv/Fm ratios. The structure of the coral microbiome community also differed between treatments and specific bioindicators were correlated with corals inoculated with pBMC (e.g., Cobetia sp.) or VC (e.g., Ruegeria sp.). Our results indicate that the microbiome in corals can be manipulated to lessen the effect of bleaching, thus helping to alleviate pathogen and temperature stresses, with the addition of BMCs representing a promising novel approach for minimizing coral mortality in the face of increasing environmental impacts
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