2298 lines
130 KiB
Text
2298 lines
130 KiB
Text
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||
\field{abstract}{Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, of variables, there are other methods, called block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. Recently, we have proposed a new mixture model called block mixture model which takes into account this situation. This model allows one to embed simultaneous clustering of objects and variables in a mixture approach. We have studied this probabilistic model under the classification likelihood approach and developed a new algorithm for simultaneous partitioning based on the classification EM algorithm. In this paper, we consider the block clustering problem under the maximum likelihood approach and the goal of our contribution is to estimate the parameters of this model. Unfortunately, the application of the EM algorithm for the block mixture model cannot be made directly; difficulties arise due to the dependence structure in the model and approximations are required. Using a variational approximation, we propose a generalized EM algorithm to estimate the parameters of the block mixture model and, to illustrate our approach, we study the case of binary data by using a Bernoulli block mixture.}
|
||
\field{eventtitle}{{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}}
|
||
\field{issn}{1939-3539}
|
||
\field{journaltitle}{IEEE Transactions on Pattern Analysis and Machine Intelligence}
|
||
\field{month}{4}
|
||
\field{number}{4}
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||
\field{title}{An {{EM}} Algorithm for the Block Mixture Model}
|
||
\field{volume}{27}
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||
\field{year}{2005}
|
||
\field{dateera}{ce}
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\field{pages}{643\bibrangedash 647}
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\verb{doi}
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\verb 10.1109/TPAMI.2005.69
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\endverb
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\verb{file}
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\verb /home/polarolouis/Zotero/storage/6IG45HH2/govaert2005.pdf.pdf;/home/polarolouis/Zotero/storage/TL8M3XRF/Govaert et Nadif - 2005 - An EM algorithm for the block mixture model.pdf;/home/polarolouis/Zotero/storage/2Y48IB26/1401917.html
|
||
\endverb
|
||
\keyw{Approximation algorithms,Classification algorithms,Clustering algorithms,Clustering methods,Data mining,EM algorithm,Index Terms- Block mixture model,Maximum likelihood estimation,Parameter estimation,Partitioning algorithms,Self organizing feature maps,Sparse matrices,variational approximation.}
|
||
\endentry
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||
\entry{doreRelativeEffectsAnthropogenic2021}{article}{}{}
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\name{author}{3}{}{%
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||
{{un=0,uniquepart=base,hash=5f8486271db5e2981426fd924aa1f23e}{%
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family={Doré},
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{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
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family={Fontaine},
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familyi={F\bibinitperiod},
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given={Colin},
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givenun=0}}%
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||
{{un=0,uniquepart=base,hash=ced1f9b5102addb42e37cc32bb0822c2}{%
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family={Thébault},
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||
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given={Elisa},
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||
giveni={E\bibinitperiod},
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||
givenun=0}}%
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}
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||
\field{abstract}{Pollinators provide crucial ecosystem services that underpin to wild plant reproduction and yields of insect-pollinated crops. Understanding the relative impacts of anthropogenic pressures and climate on the structure of plant–pollinator interaction networks is vital considering ongoing global change and pollinator decline. Our ability to predict the consequences of global change for pollinator assemblages worldwide requires global syntheses, but these analytical approaches may be hindered by variable methods among studies that either invalidate comparisons or mask biological phenomena. Here we conducted a synthetic analysis that assesses the relative impact of anthropogenic pressures and climatic variability, and accounts for heterogeneity in sampling methodology to reveal network responses at the global scale. We analyzed an extensive dataset, comprising 295 networks over 123 locations all over the world, and reporting over 50,000 interactions between flowering plant species and their insect visitors. Our study revealed that anthropogenic pressures correlate with an increase in generalism in pollination networks while pollinator richness and taxonomic composition are more related to climatic variables with an increase in dipteran pollinator richness associated with cooler temperatures. The contrasting response of species richness and generalism of the plant–pollinator networks stresses the importance of considering interaction network structure alongside diversity in ecological monitoring. In addition, differences in sampling design explained more variation than anthropogenic pressures or climate on both pollination networks richness and generalism, highlighting the crucial need to report and incorporate sampling design in macroecological comparative studies of pollination networks. As a whole, our study reveals a potential human impact on pollination networks at a global scale. However, further research is needed to evaluate potential consequences of loss of specialist species and their unique ecological interactions and evolutionary pathways on the ecosystem pollination function at a global scale.}
|
||
\field{issn}{1365-2486}
|
||
\field{journaltitle}{Global Change Biology}
|
||
\field{langid}{english}
|
||
\field{number}{6}
|
||
\field{title}{Relative Effects of Anthropogenic Pressures, Climate, and Sampling Design on the Structure of Pollination Networks at the Global Scale}
|
||
\field{urlday}{21}
|
||
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|
||
\field{urlyear}{2023}
|
||
\field{volume}{27}
|
||
\field{year}{2021}
|
||
\field{dateera}{ce}
|
||
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|
||
\field{pages}{1266\bibrangedash 1280}
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||
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||
\verb{doi}
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||
\verb 10.1111/gcb.15474
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||
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||
\verb{file}
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\verb /home/polarolouis/Zotero/storage/89ZXBJQP/10.1111@gcb.15474.pdf.pdf;/home/polarolouis/Zotero/storage/IVR6RGG7/Doré et al. - 2021 - Relative effects of anthropogenic pressures, clima.pdf;/home/polarolouis/Zotero/storage/WSJ4DV98/gcb.html
|
||
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||
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\verb https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474
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||
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|
||
\verb{url}
|
||
\verb https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474
|
||
\endverb
|
||
\keyw{anthropogenic pressures,climate,connectance,data,generalism,human impacts,plant-pollinator,pollination networks,richness,sampling effects,specialization}
|
||
\endentry
|
||
\entry{thebaultDatabasePlantpollinatorNetworks2020}{dataset}{}{}
|
||
\name{author}{2}{}{%
|
||
{{un=0,uniquepart=base,hash=ced1f9b5102addb42e37cc32bb0822c2}{%
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||
family={Thébault},
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||
familyi={T\bibinitperiod},
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||
given={Elisa},
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||
givenun=0}}%
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{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
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family={Fontaine},
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||
familyi={F\bibinitperiod},
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||
given={Colin},
|
||
giveni={C\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\list{publisher}{1}{%
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||
{Zenodo}%
|
||
}
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||
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||
\strng{fullhash}{a66b587a0ffe44f623fa568a960cd9d9}
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||
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|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.}
|
||
\field{day}{1}
|
||
\field{month}{12}
|
||
\field{title}{A Database of Plant-Pollinator Networks}
|
||
\field{urlday}{21}
|
||
\field{urlmonth}{6}
|
||
\field{urlyear}{2023}
|
||
\field{version}{1}
|
||
\field{year}{2020}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\verb{doi}
|
||
\verb 10.5281/zenodo.4300427
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb https://zenodo.org/record/4300427
|
||
\endverb
|
||
\verb{url}
|
||
\verb https://zenodo.org/record/4300427
|
||
\endverb
|
||
\keyw{diversity,flower visitors,mutualistic network,plant-pollinator interaction}
|
||
\endentry
|
||
\entry{chabert-liddellLearningCommonStructures2024}{article}{}{}
|
||
\name{author}{3}{}{%
|
||
{{un=0,uniquepart=base,hash=b2590d483a7fe284c2cdda3920206a4e}{%
|
||
family={Chabert-Liddell},
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||
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||
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||
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||
{{un=0,uniquepart=base,hash=7fecb6ce38c5ec9d4555962d959d2379}{%
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family={Barbillon},
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||
familyi={B\bibinitperiod},
|
||
given={Pierre},
|
||
giveni={P\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=06c8f96f3a1aba5140a38275380f781f}{%
|
||
family={Donnet},
|
||
familyi={D\bibinitperiod},
|
||
given={Sophie},
|
||
giveni={S\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\list{publisher}{1}{%
|
||
{Institute of Mathematical Statistics}%
|
||
}
|
||
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||
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|
||
\field{abstract}{Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into subcollections of structurally homogeneous networks. We tackle these two questions with a probabilistic model-based approach. We propose an extension of the stochastic block model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational expectation-maximization (EM) algorithm. We derive an ad hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.}
|
||
\field{issn}{1932-6157, 1941-7330}
|
||
\field{journaltitle}{The Annals of Applied Statistics}
|
||
\field{month}{6}
|
||
\field{number}{2}
|
||
\field{title}{Learning Common Structures in a Collection of Networks. {{An}} Application to Food Webs}
|
||
\field{urlday}{16}
|
||
\field{urlmonth}{5}
|
||
\field{urlyear}{2024}
|
||
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|
||
\field{year}{2024}
|
||
\field{dateera}{ce}
|
||
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|
||
\field{pages}{1213\bibrangedash 1235}
|
||
\range{pages}{23}
|
||
\verb{doi}
|
||
\verb 10.1214/23-AOAS1831
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||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/4USKD3WW/Chabert-Liddell et al. - 2024 - Learning common structures in a collection of netw.pdf
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-2/Learning-common-structures-in-a-collection-of-networks-An-application/10.1214/23-AOAS1831.full
|
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\endverb
|
||
\verb{url}
|
||
\verb https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-2/Learning-common-structures-in-a-collection-of-networks-An-application/10.1214/23-AOAS1831.full
|
||
\endverb
|
||
\keyw{clustering,ecology,latent variable models,networks,Stochastic block model}
|
||
\endentry
|
||
\entry{celisseConsistencyMaximumlikelihoodVariational2012}{article}{}{}
|
||
\name{author}{3}{}{%
|
||
{{un=0,uniquepart=base,hash=3250ac136af0d1776387e11bb5155201}{%
|
||
family={Celisse},
|
||
familyi={C\bibinitperiod},
|
||
given={Alain},
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||
giveni={A\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=7811e291e538910fdb86db8a8aa0c679}{%
|
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family={Daudin},
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||
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||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=30e267e4abc8b1d5dc28ae009fcbcfdf}{%
|
||
family={Pierre},
|
||
familyi={P\bibinitperiod},
|
||
given={Laurent},
|
||
giveni={L\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\list{publisher}{1}{%
|
||
{Institute of Mathematical Statistics and Bernoulli Society}%
|
||
}
|
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||
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||
\field{abstract}{The stochastic block model (SBM) is a probabilistic model designed to describe heterogeneous directed and undirected graphs. In this paper, we address the asymptotic inference in SBM by use of maximum-likelihood and variational approaches. The identifiability of SBM is proved while asymptotic properties of maximum-likelihood and variational estimators are derived. In particular, the consistency of these estimators is settled for the probability of an edge between two vertices (and for the group proportions at the price of an additional assumption), which is to the best of our knowledge the first result of this type for variational estimators in random graphs.}
|
||
\field{issn}{1935-7524, 1935-7524}
|
||
\field{issue}{none}
|
||
\field{journaltitle}{Electronic Journal of Statistics}
|
||
\field{month}{1}
|
||
\field{title}{Consistency of Maximum-Likelihood and Variational Estimators in the Stochastic Block Model}
|
||
\field{urlday}{6}
|
||
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||
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||
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||
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\field{abstract}{En apprentissage automatique, un auto-encodeur variationnel (ou VAE de l'anglais variational auto encoder), est une architecture de réseau de neurones artificiels introduite en 2013 par D. Kingma et M. Welling, appartenant aux familles des modèles graphiques probabilistes et des méthodes bayésiennes variationnelles. Les VAE sont souvent rapprochés des autoencodeurs, en raison de leur architectures similaires. Leur utilisation et leur formulation mathématiques sont cependant différentes. Les auto-encodeurs variationnels permettent de formuler un problème d'inférence statistique (par exemple, déduire la valeur d'une variable aléatoire à partir d'une autre variable aléatoire) en un problème d'optimisation statistique (c'est-à-dire trouver les valeurs de paramètres qui minimisent une fonction objectif). Ils représentent une fonction associant à une valeur d'entrée une distribution latente multivariée, qui n'est pas directement observée mais déduite depuis un modèle mathématique à partir de la distribution d'autres variables. Bien que ce type de modèle ait été initialement conçu pour l'apprentissage non supervisé, son efficacité a été prouvée pour l'apprentissage semi-supervisé, et l'apprentissage supervisé.}
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\field{abstract}{How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.}
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\field{title}{Variational {{Graph Auto-Encoders}}}
|
||
\field{urlday}{14}
|
||
\field{urlmonth}{5}
|
||
\field{urlyear}{2024}
|
||
\field{year}{2016}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\verb{doi}
|
||
\verb 10.48550/arXiv.1611.07308
|
||
\endverb
|
||
\verb{eprint}
|
||
\verb 1611.07308
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/MSK48ZUE/Kipf et Welling - 2016 - Variational Graph Auto-Encoders.pdf;/home/polarolouis/Zotero/storage/3VJBSGI3/1611.html
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb http://arxiv.org/abs/1611.07308
|
||
\endverb
|
||
\verb{url}
|
||
\verb http://arxiv.org/abs/1611.07308
|
||
\endverb
|
||
\keyw{Computer Science - Machine Learning,Statistics - Machine Learning}
|
||
\endentry
|
||
\entry{yangDeepLatentSpace2024}{article}{}{}
|
||
\name{author}{3}{}{%
|
||
{{un=0,uniquepart=base,hash=5c4c1190a03bf4cd8d67bbdabf204679}{%
|
||
family={Yang},
|
||
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|
||
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|
||
giveni={H\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=95205c79f1b50db92a190a8fa775a848}{%
|
||
family={Kong},
|
||
familyi={K\bibinitperiod},
|
||
given={Qingchao},
|
||
giveni={Q\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=d040f29a3a9bdf06f9afa70b2f266303}{%
|
||
family={Mao},
|
||
familyi={M\bibinitperiod},
|
||
given={Wenji},
|
||
giveni={W\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
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||
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|
||
\strng{authorbibnamehash}{2040be8e529676f3e058b1353ff2d9f6}
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||
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|
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|
||
\field{extradatescope}{labelyear}
|
||
\field{labeldatesource}{}
|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{Graph representation learning is a fundamental problem for modeling relational data and benefits a number of downstream applications. Traditional Bayesian-based graph models and recent deep learning based GNN either suffer from impracticability or lack interpretability, thus combined models for undirected graphs have been proposed to overcome the weaknesses. As a large portion of real-world graphs are directed graphs (of which undirected graphs are special cases), in this paper, we propose a Deep Latent Space Model (DLSM) for directed graphs to incorporate the traditional latent variable based generative model into deep learning frameworks. Our proposed model consists of a graph convolutional network (GCN) encoder and a stochastic decoder, which are layer-wise connected by a hierarchical variational auto-encoder architecture. By specifically modeling the degree heterogeneity using node random factors, our model possesses better interpretability in both community structure and degree heterogeneity. For fast inference, the stochastic gradient variational Bayes (SGVB) is adopted using a non-iterative recognition model, which is much more scalable than traditional MCMC-based methods. The experiments on real-world datasets show that the proposed model achieves the state-of-the-art performances on both link prediction and community detection tasks while learning interpretable node embeddings. The source code is available at https://github.com/upperr/DLSM.}
|
||
\field{eprintclass}{cs, stat}
|
||
\field{eprinttype}{arxiv}
|
||
\field{issn}{09252312}
|
||
\field{journaltitle}{Neurocomputing}
|
||
\field{langid}{english}
|
||
\field{month}{4}
|
||
\field{title}{A {{Deep Latent Space Model}} for {{Graph Representation Learning}}}
|
||
\field{urlday}{20}
|
||
\field{urlmonth}{5}
|
||
\field{urlyear}{2024}
|
||
\field{volume}{576}
|
||
\field{year}{2024}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\field{pages}{127342}
|
||
\range{pages}{1}
|
||
\verb{doi}
|
||
\verb 10.1016/j.neucom.2024.127342
|
||
\endverb
|
||
\verb{eprint}
|
||
\verb 2106.11721
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/XNVMI2D7/Yang et al. - 2024 - A Deep Latent Space Model for Graph Representation.pdf
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb http://arxiv.org/abs/2106.11721
|
||
\endverb
|
||
\verb{url}
|
||
\verb http://arxiv.org/abs/2106.11721
|
||
\endverb
|
||
\keyw{Computer Science - Machine Learning,Statistics - Machine Learning}
|
||
\endentry
|
||
\entry{matchadoNetworkAnalysisMethods2021}{article}{}{}
|
||
\name{author}{7}{}{%
|
||
{{un=0,uniquepart=base,hash=177b09aedc133f79d0f85c2dfc292195}{%
|
||
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|
||
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|
||
given={Monica\bibnamedelima Steffi},
|
||
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|
||
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|
||
{{un=0,uniquepart=base,hash=0351e2aa2bc73ea26b62b7d3a25a19cf}{%
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
{{un=0,uniquepart=base,hash=7336b2cc670ef3ba710d11a9a6188b0a}{%
|
||
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|
||
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|
||
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|
||
giveni={S\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=16c8e8d9218bbb34cc4ed6caea1e09fc}{%
|
||
family={Kacprowski},
|
||
familyi={K\bibinitperiod},
|
||
given={Tim},
|
||
giveni={T\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=126dc2bd956d9c46285087fd8d139873}{%
|
||
family={Baumbach},
|
||
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|
||
given={Jan},
|
||
giveni={J\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=7cbf22900ebdd03028f74d4b0e45da4f}{%
|
||
family={Haller},
|
||
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|
||
given={Dirk},
|
||
giveni={D\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=06ccacd7416847b34986e0192dcf68e4}{%
|
||
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|
||
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|
||
given={Markus},
|
||
giveni={M\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
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|
||
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|
||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{shorttitle}
|
||
\field{abstract}{Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.}
|
||
\field{day}{1}
|
||
\field{issn}{2001-0370}
|
||
\field{journaltitle}{Computational and Structural Biotechnology Journal}
|
||
\field{month}{1}
|
||
\field{shorttitle}{Network Analysis Methods for Studying Microbial Communities}
|
||
\field{title}{Network Analysis Methods for Studying Microbial Communities: {{A}} Mini Review}
|
||
\field{urlday}{16}
|
||
\field{urlmonth}{5}
|
||
\field{urlyear}{2024}
|
||
\field{volume}{19}
|
||
\field{year}{2021}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\field{pages}{2687\bibrangedash 2698}
|
||
\range{pages}{12}
|
||
\verb{doi}
|
||
\verb 10.1016/j.csbj.2021.05.001
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/NAEQFHE8/j.csbj.2021.05.001.pdf.pdf;/home/polarolouis/Zotero/storage/SXJYNPP7/Matchado et al. - 2021 - Network analysis methods for studying microbial co.pdf;/home/polarolouis/Zotero/storage/B6NZVP7Y/S2001037021001823.html
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb https://www.sciencedirect.com/science/article/pii/S2001037021001823
|
||
\endverb
|
||
\verb{url}
|
||
\verb https://www.sciencedirect.com/science/article/pii/S2001037021001823
|
||
\endverb
|
||
\keyw{Microbial co-occurrence networks,Microbial interactions,Network analysis,Trans-kingdom interactions}
|
||
\endentry
|
||
\enddatalist
|
||
\datalist[entry]{none/global//global/global/global}
|
||
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|
||
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|
||
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|
||
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|
||
\field{labeltitlesource}{title}
|
||
\field{title}{Web of {{Life}}: Ecological Networks Database}
|
||
\field{urlday}{17}
|
||
\field{urlmonth}{6}
|
||
\field{urlyear}{2023}
|
||
\field{urldateera}{ce}
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/9WZE8QLQ/map.html
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb https://www.web-of-life.es/map.php
|
||
\endverb
|
||
\verb{url}
|
||
\verb https://www.web-of-life.es/map.php
|
||
\endverb
|
||
\keyw{networks,site}
|
||
\endentry
|
||
\entry{govaertEMAlgorithmBlock2005}{article}{}{}
|
||
\name{author}{2}{}{%
|
||
{{un=0,uniquepart=base,hash=e362e82efc56e96877c249de27098dd7}{%
|
||
family={Govaert},
|
||
familyi={G\bibinitperiod},
|
||
given={G.},
|
||
giveni={G\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=5554bf45de9d6150691084d7cde5594c}{%
|
||
family={Nadif},
|
||
familyi={N\bibinitperiod},
|
||
given={M.},
|
||
giveni={M\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\strng{namehash}{cf148152bf4f33b329bfd7d07cf74c02}
|
||
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|
||
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||
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||
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
\field{labeldatesource}{}
|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, of variables, there are other methods, called block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. Recently, we have proposed a new mixture model called block mixture model which takes into account this situation. This model allows one to embed simultaneous clustering of objects and variables in a mixture approach. We have studied this probabilistic model under the classification likelihood approach and developed a new algorithm for simultaneous partitioning based on the classification EM algorithm. In this paper, we consider the block clustering problem under the maximum likelihood approach and the goal of our contribution is to estimate the parameters of this model. Unfortunately, the application of the EM algorithm for the block mixture model cannot be made directly; difficulties arise due to the dependence structure in the model and approximations are required. Using a variational approximation, we propose a generalized EM algorithm to estimate the parameters of the block mixture model and, to illustrate our approach, we study the case of binary data by using a Bernoulli block mixture.}
|
||
\field{eventtitle}{{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}}
|
||
\field{issn}{1939-3539}
|
||
\field{journaltitle}{IEEE Transactions on Pattern Analysis and Machine Intelligence}
|
||
\field{month}{4}
|
||
\field{number}{4}
|
||
\field{title}{An {{EM}} Algorithm for the Block Mixture Model}
|
||
\field{volume}{27}
|
||
\field{year}{2005}
|
||
\field{dateera}{ce}
|
||
\field{pages}{643\bibrangedash 647}
|
||
\range{pages}{5}
|
||
\verb{doi}
|
||
\verb 10.1109/TPAMI.2005.69
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/6IG45HH2/govaert2005.pdf.pdf;/home/polarolouis/Zotero/storage/TL8M3XRF/Govaert et Nadif - 2005 - An EM algorithm for the block mixture model.pdf;/home/polarolouis/Zotero/storage/2Y48IB26/1401917.html
|
||
\endverb
|
||
\keyw{Approximation algorithms,Classification algorithms,Clustering algorithms,Clustering methods,Data mining,EM algorithm,Index Terms- Block mixture model,Maximum likelihood estimation,Parameter estimation,Partitioning algorithms,Self organizing feature maps,Sparse matrices,variational approximation.}
|
||
\endentry
|
||
\entry{doreRelativeEffectsAnthropogenic2021}{article}{}{}
|
||
\name{author}{3}{}{%
|
||
{{un=0,uniquepart=base,hash=5f8486271db5e2981426fd924aa1f23e}{%
|
||
family={Doré},
|
||
familyi={D\bibinitperiod},
|
||
given={Maël},
|
||
giveni={M\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
|
||
family={Fontaine},
|
||
familyi={F\bibinitperiod},
|
||
given={Colin},
|
||
giveni={C\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=ced1f9b5102addb42e37cc32bb0822c2}{%
|
||
family={Thébault},
|
||
familyi={T\bibinitperiod},
|
||
given={Elisa},
|
||
giveni={E\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\strng{namehash}{c9a7cb75b065abd1e803419ba2751185}
|
||
\strng{fullhash}{a13d5d8e849820e44f068077f3aef7c1}
|
||
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|
||
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|
||
\strng{authorbibnamehash}{a13d5d8e849820e44f068077f3aef7c1}
|
||
\strng{authornamehash}{c9a7cb75b065abd1e803419ba2751185}
|
||
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|
||
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|
||
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|
||
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|
||
\field{extradatescope}{labelyear}
|
||
\field{labeldatesource}{}
|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{Pollinators provide crucial ecosystem services that underpin to wild plant reproduction and yields of insect-pollinated crops. Understanding the relative impacts of anthropogenic pressures and climate on the structure of plant–pollinator interaction networks is vital considering ongoing global change and pollinator decline. Our ability to predict the consequences of global change for pollinator assemblages worldwide requires global syntheses, but these analytical approaches may be hindered by variable methods among studies that either invalidate comparisons or mask biological phenomena. Here we conducted a synthetic analysis that assesses the relative impact of anthropogenic pressures and climatic variability, and accounts for heterogeneity in sampling methodology to reveal network responses at the global scale. We analyzed an extensive dataset, comprising 295 networks over 123 locations all over the world, and reporting over 50,000 interactions between flowering plant species and their insect visitors. Our study revealed that anthropogenic pressures correlate with an increase in generalism in pollination networks while pollinator richness and taxonomic composition are more related to climatic variables with an increase in dipteran pollinator richness associated with cooler temperatures. The contrasting response of species richness and generalism of the plant–pollinator networks stresses the importance of considering interaction network structure alongside diversity in ecological monitoring. In addition, differences in sampling design explained more variation than anthropogenic pressures or climate on both pollination networks richness and generalism, highlighting the crucial need to report and incorporate sampling design in macroecological comparative studies of pollination networks. As a whole, our study reveals a potential human impact on pollination networks at a global scale. However, further research is needed to evaluate potential consequences of loss of specialist species and their unique ecological interactions and evolutionary pathways on the ecosystem pollination function at a global scale.}
|
||
\field{issn}{1365-2486}
|
||
\field{journaltitle}{Global Change Biology}
|
||
\field{langid}{english}
|
||
\field{number}{6}
|
||
\field{title}{Relative Effects of Anthropogenic Pressures, Climate, and Sampling Design on the Structure of Pollination Networks at the Global Scale}
|
||
\field{urlday}{21}
|
||
\field{urlmonth}{6}
|
||
\field{urlyear}{2023}
|
||
\field{volume}{27}
|
||
\field{year}{2021}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\field{pages}{1266\bibrangedash 1280}
|
||
\range{pages}{15}
|
||
\verb{doi}
|
||
\verb 10.1111/gcb.15474
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/89ZXBJQP/10.1111@gcb.15474.pdf.pdf;/home/polarolouis/Zotero/storage/IVR6RGG7/Doré et al. - 2021 - Relative effects of anthropogenic pressures, clima.pdf;/home/polarolouis/Zotero/storage/WSJ4DV98/gcb.html
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474
|
||
\endverb
|
||
\verb{url}
|
||
\verb https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474
|
||
\endverb
|
||
\keyw{anthropogenic pressures,climate,connectance,data,generalism,human impacts,plant-pollinator,pollination networks,richness,sampling effects,specialization}
|
||
\endentry
|
||
\entry{thebaultDatabasePlantpollinatorNetworks2020}{dataset}{}{}
|
||
\name{author}{2}{}{%
|
||
{{un=0,uniquepart=base,hash=ced1f9b5102addb42e37cc32bb0822c2}{%
|
||
family={Thébault},
|
||
familyi={T\bibinitperiod},
|
||
given={Elisa},
|
||
giveni={E\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
|
||
family={Fontaine},
|
||
familyi={F\bibinitperiod},
|
||
given={Colin},
|
||
giveni={C\bibinitperiod},
|
||
givenun=0}}%
|
||
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\field{abstract}{This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.}
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\field{abstract}{Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into subcollections of structurally homogeneous networks. We tackle these two questions with a probabilistic model-based approach. We propose an extension of the stochastic block model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational expectation-maximization (EM) algorithm. We derive an ad hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.}
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\field{abstract}{The stochastic block model (SBM) is a probabilistic model designed to describe heterogeneous directed and undirected graphs. In this paper, we address the asymptotic inference in SBM by use of maximum-likelihood and variational approaches. The identifiability of SBM is proved while asymptotic properties of maximum-likelihood and variational estimators are derived. In particular, the consistency of these estimators is settled for the probability of an edge between two vertices (and for the group proportions at the price of an additional assumption), which is to the best of our knowledge the first result of this type for variational estimators in random graphs.}
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||
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\field{abstract}{This paper deals with estimation and model selection in the Latent Block Model (LBM) for categorical data. First, after providing sufficient conditions ensuring the identifiability of this model, we generalise estimation procedures and model selection criteria derived for binary data. Secondly, we develop Bayesian inference through Gibbs sampling and with a well calibrated non informative prior distribution, in order to get the MAP estimator: this is proved to avoid the traps encountered by the LBM with the maximum likelihood methodology. Then model selection criteria are presented. In particular an exact expression of the integrated completed likelihood criterion requiring no asymptotic approximation is derived. Finally numerical experiments on both simulated and real data sets highlight the appeal of the proposed estimation and model selection procedures.}
|
||
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|
||
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|
||
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||
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|
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||
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|
||
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||
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|
||
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||
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|
||
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|
||
\keyw{Bayesian inference,BIC criterion,EM algorithm,Gibbs sampling,Integrated completed likelihood,Stochastic EM,Variational approximation}
|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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\field{abstract}{En apprentissage automatique, un auto-encodeur variationnel (ou VAE de l'anglais variational auto encoder), est une architecture de réseau de neurones artificiels introduite en 2013 par D. Kingma et M. Welling, appartenant aux familles des modèles graphiques probabilistes et des méthodes bayésiennes variationnelles. Les VAE sont souvent rapprochés des autoencodeurs, en raison de leur architectures similaires. Leur utilisation et leur formulation mathématiques sont cependant différentes. Les auto-encodeurs variationnels permettent de formuler un problème d'inférence statistique (par exemple, déduire la valeur d'une variable aléatoire à partir d'une autre variable aléatoire) en un problème d'optimisation statistique (c'est-à-dire trouver les valeurs de paramètres qui minimisent une fonction objectif). Ils représentent une fonction associant à une valeur d'entrée une distribution latente multivariée, qui n'est pas directement observée mais déduite depuis un modèle mathématique à partir de la distribution d'autres variables. Bien que ce type de modèle ait été initialement conçu pour l'apprentissage non supervisé, son efficacité a été prouvée pour l'apprentissage semi-supervisé, et l'apprentissage supervisé.}
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\field{abstract}{Graph representation learning is a fundamental problem for modeling relational data and benefits a number of downstream applications. Traditional Bayesian-based graph models and recent deep learning based GNN either suffer from impracticability or lack interpretability, thus combined models for undirected graphs have been proposed to overcome the weaknesses. As a large portion of real-world graphs are directed graphs (of which undirected graphs are special cases), in this paper, we propose a Deep Latent Space Model (DLSM) for directed graphs to incorporate the traditional latent variable based generative model into deep learning frameworks. Our proposed model consists of a graph convolutional network (GCN) encoder and a stochastic decoder, which are layer-wise connected by a hierarchical variational auto-encoder architecture. By specifically modeling the degree heterogeneity using node random factors, our model possesses better interpretability in both community structure and degree heterogeneity. For fast inference, the stochastic gradient variational Bayes (SGVB) is adopted using a non-iterative recognition model, which is much more scalable than traditional MCMC-based methods. The experiments on real-world datasets show that the proposed model achieves the state-of-the-art performances on both link prediction and community detection tasks while learning interpretable node embeddings. The source code is available at https://github.com/upperr/DLSM.}
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\field{abstract}{Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.}
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||
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\endentry
|
||
\enddatalist
|
||
\endrefsection
|
||
|
||
\refsection{2}
|
||
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||
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||
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||
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|
||
\field{abstract}{Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, of variables, there are other methods, called block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. Recently, we have proposed a new mixture model called block mixture model which takes into account this situation. This model allows one to embed simultaneous clustering of objects and variables in a mixture approach. We have studied this probabilistic model under the classification likelihood approach and developed a new algorithm for simultaneous partitioning based on the classification EM algorithm. In this paper, we consider the block clustering problem under the maximum likelihood approach and the goal of our contribution is to estimate the parameters of this model. Unfortunately, the application of the EM algorithm for the block mixture model cannot be made directly; difficulties arise due to the dependence structure in the model and approximations are required. Using a variational approximation, we propose a generalized EM algorithm to estimate the parameters of the block mixture model and, to illustrate our approach, we study the case of binary data by using a Bernoulli block mixture.}
|
||
\field{eventtitle}{{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}}
|
||
\field{issn}{1939-3539}
|
||
\field{journaltitle}{IEEE Transactions on Pattern Analysis and Machine Intelligence}
|
||
\field{month}{4}
|
||
\field{number}{4}
|
||
\field{title}{An {{EM}} Algorithm for the Block Mixture Model}
|
||
\field{volume}{27}
|
||
\field{year}{2005}
|
||
\field{dateera}{ce}
|
||
\field{pages}{643\bibrangedash 647}
|
||
\range{pages}{5}
|
||
\verb{doi}
|
||
\verb 10.1109/TPAMI.2005.69
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/6IG45HH2/govaert2005.pdf.pdf;/home/polarolouis/Zotero/storage/TL8M3XRF/Govaert et Nadif - 2005 - An EM algorithm for the block mixture model.pdf;/home/polarolouis/Zotero/storage/2Y48IB26/1401917.html
|
||
\endverb
|
||
\keyw{Approximation algorithms,Classification algorithms,Clustering algorithms,Clustering methods,Data mining,EM algorithm,Index Terms- Block mixture model,Maximum likelihood estimation,Parameter estimation,Partitioning algorithms,Self organizing feature maps,Sparse matrices,variational approximation.}
|
||
\endentry
|
||
\entry{chabert-liddellLearningCommonStructures2023}{online}{}{}
|
||
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|
||
{{un=0,uniquepart=base,hash=b2590d483a7fe284c2cdda3920206a4e}{%
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||
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|
||
{{un=0,uniquepart=base,hash=7fecb6ce38c5ec9d4555962d959d2379}{%
|
||
family={Barbillon},
|
||
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|
||
given={Pierre},
|
||
giveni={P\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=06c8f96f3a1aba5140a38275380f781f}{%
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
\field{abstract}{Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into sub-collections of structurally homogeneous networks. We tackle these two questions with a probabilistic model based approach. We propose an extension of the Stochastic Block Model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational Expectation-Maximization (EM) algorithm. We derive an ad-hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.}
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||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
\verb{doi}
|
||
\verb 10.48550/arXiv.2206.00560
|
||
\endverb
|
||
\verb{eprint}
|
||
\verb 2206.00560
|
||
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|
||
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|
||
\verb /home/polarolouis/Zotero/storage/M74TXGCF/Chabert-Liddell et al. - 2023 - Learning common structures in a collection of netw.pdf;/home/polarolouis/Zotero/storage/A35M8KNP/2206.html
|
||
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|
||
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|
||
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|
||
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|
||
\verb{url}
|
||
\verb http://arxiv.org/abs/2206.00560
|
||
\endverb
|
||
\keyw{Statistics - Applications,Statistics - Methodology}
|
||
\endentry
|
||
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|
||
\name{author}{3}{}{%
|
||
{{un=0,uniquepart=base,hash=5f8486271db5e2981426fd924aa1f23e}{%
|
||
family={Doré},
|
||
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|
||
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|
||
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|
||
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|
||
{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
|
||
family={Fontaine},
|
||
familyi={F\bibinitperiod},
|
||
given={Colin},
|
||
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|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=ced1f9b5102addb42e37cc32bb0822c2}{%
|
||
family={Thébault},
|
||
familyi={T\bibinitperiod},
|
||
given={Elisa},
|
||
giveni={E\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\strng{namehash}{c9a7cb75b065abd1e803419ba2751185}
|
||
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||
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|
||
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|
||
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|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{Pollinators provide crucial ecosystem services that underpin to wild plant reproduction and yields of insect-pollinated crops. Understanding the relative impacts of anthropogenic pressures and climate on the structure of plant–pollinator interaction networks is vital considering ongoing global change and pollinator decline. Our ability to predict the consequences of global change for pollinator assemblages worldwide requires global syntheses, but these analytical approaches may be hindered by variable methods among studies that either invalidate comparisons or mask biological phenomena. Here we conducted a synthetic analysis that assesses the relative impact of anthropogenic pressures and climatic variability, and accounts for heterogeneity in sampling methodology to reveal network responses at the global scale. We analyzed an extensive dataset, comprising 295 networks over 123 locations all over the world, and reporting over 50,000 interactions between flowering plant species and their insect visitors. Our study revealed that anthropogenic pressures correlate with an increase in generalism in pollination networks while pollinator richness and taxonomic composition are more related to climatic variables with an increase in dipteran pollinator richness associated with cooler temperatures. The contrasting response of species richness and generalism of the plant–pollinator networks stresses the importance of considering interaction network structure alongside diversity in ecological monitoring. In addition, differences in sampling design explained more variation than anthropogenic pressures or climate on both pollination networks richness and generalism, highlighting the crucial need to report and incorporate sampling design in macroecological comparative studies of pollination networks. As a whole, our study reveals a potential human impact on pollination networks at a global scale. However, further research is needed to evaluate potential consequences of loss of specialist species and their unique ecological interactions and evolutionary pathways on the ecosystem pollination function at a global scale.}
|
||
\field{issn}{1365-2486}
|
||
\field{journaltitle}{Global Change Biology}
|
||
\field{langid}{english}
|
||
\field{number}{6}
|
||
\field{title}{Relative Effects of Anthropogenic Pressures, Climate, and Sampling Design on the Structure of Pollination Networks at the Global Scale}
|
||
\field{urlday}{21}
|
||
\field{urlmonth}{6}
|
||
\field{urlyear}{2023}
|
||
\field{volume}{27}
|
||
\field{year}{2021}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\field{pages}{1266\bibrangedash 1280}
|
||
\range{pages}{15}
|
||
\verb{doi}
|
||
\verb 10.1111/gcb.15474
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/89ZXBJQP/10.1111@gcb.15474.pdf.pdf;/home/polarolouis/Zotero/storage/IVR6RGG7/Doré et al. - 2021 - Relative effects of anthropogenic pressures, clima.pdf;/home/polarolouis/Zotero/storage/WSJ4DV98/gcb.html
|
||
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|
||
\verb{urlraw}
|
||
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|
||
\endverb
|
||
\verb{url}
|
||
\verb https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474
|
||
\endverb
|
||
\keyw{anthropogenic pressures,climate,connectance,data,generalism,human impacts,plant-pollinator,pollination networks,richness,sampling effects,specialization}
|
||
\endentry
|
||
\entry{thebaultDatabasePlantpollinatorNetworks2020}{dataset}{}{}
|
||
\name{author}{2}{}{%
|
||
{{un=0,uniquepart=base,hash=ced1f9b5102addb42e37cc32bb0822c2}{%
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||
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||
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||
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|
||
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||
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|
||
{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
|
||
family={Fontaine},
|
||
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|
||
given={Colin},
|
||
giveni={C\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\list{publisher}{1}{%
|
||
{Zenodo}%
|
||
}
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|
||
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|
||
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|
||
\field{labeldatesource}{}
|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.}
|
||
\field{day}{1}
|
||
\field{month}{12}
|
||
\field{title}{A Database of Plant-Pollinator Networks}
|
||
\field{urlday}{21}
|
||
\field{urlmonth}{6}
|
||
\field{urlyear}{2023}
|
||
\field{version}{1}
|
||
\field{year}{2020}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\verb{doi}
|
||
\verb 10.5281/zenodo.4300427
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb https://zenodo.org/record/4300427
|
||
\endverb
|
||
\verb{url}
|
||
\verb https://zenodo.org/record/4300427
|
||
\endverb
|
||
\keyw{diversity,flower visitors,mutualistic network,plant-pollinator interaction}
|
||
\endentry
|
||
\entry{anakokDisentanglingStructureEcological2022}{online}{}{}
|
||
\name{author}{4}{}{%
|
||
{{un=0,uniquepart=base,hash=fe9e51991c906363fdb9789340eb02b8}{%
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
{{un=0,uniquepart=base,hash=7fecb6ce38c5ec9d4555962d959d2379}{%
|
||
family={Barbillon},
|
||
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|
||
given={Pierre},
|
||
giveni={P\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
|
||
family={Fontaine},
|
||
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|
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|
||
giveni={C\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=f23dbf986aa073f9022baede42ef0479}{%
|
||
family={Thebault},
|
||
familyi={T\bibinitperiod},
|
||
given={Elisa},
|
||
giveni={E\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\strng{namehash}{91ac6a753125108a3ce0782cad59a3d8}
|
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|
||
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|
||
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||
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|
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|
||
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|
||
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|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{The structure of a bipartite interaction network can be described by providing a clustering for each of the two types of nodes. Such clusterings are outputted by fitting a Latent Block Model (LBM) on an observed network that comes from a sampling of species interactions in the field. However, the sampling is limited and possibly uneven. This may jeopardize the fit of the LBM and then the description of the structure of the network by detecting structures which result from the sampling and not from actual underlying ecological phenomena. If the observed interaction network consists of a weighted bipartite network where the number of observed interactions between two species is available, the sampling efforts for all species can be estimated and used to correct the LBM fit. We propose to combine an observation model that accounts for sampling and an LBM for describing the structure of underlying possible ecological interactions. We develop an original inference procedure for this model, the efficiency of which is demonstrated in simulation studies. The practical interest in ecology of our model is highlighted on a large dataset of plant-pollinator network.}
|
||
\field{day}{29}
|
||
\field{eprintclass}{stat}
|
||
\field{eprinttype}{arxiv}
|
||
\field{langid}{english}
|
||
\field{month}{11}
|
||
\field{title}{Disentangling the Structure of Ecological Bipartite Networks from Observation Processes}
|
||
\field{urlday}{14}
|
||
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|
||
\field{urlyear}{2023}
|
||
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|
||
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|
||
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|
||
\verb{eprint}
|
||
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|
||
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|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/LQ3FINZG/Anakok et al. - 2022 - Disentangling the structure of ecological bipartit.pdf
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb http://arxiv.org/abs/2211.16364
|
||
\endverb
|
||
\verb{url}
|
||
\verb http://arxiv.org/abs/2211.16364
|
||
\endverb
|
||
\keyw{Statistics - Methodology}
|
||
\endentry
|
||
\enddatalist
|
||
\datalist[entry]{none/global//global/global/global}
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
\field{abstract}{Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, of variables, there are other methods, called block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. Recently, we have proposed a new mixture model called block mixture model which takes into account this situation. This model allows one to embed simultaneous clustering of objects and variables in a mixture approach. We have studied this probabilistic model under the classification likelihood approach and developed a new algorithm for simultaneous partitioning based on the classification EM algorithm. In this paper, we consider the block clustering problem under the maximum likelihood approach and the goal of our contribution is to estimate the parameters of this model. Unfortunately, the application of the EM algorithm for the block mixture model cannot be made directly; difficulties arise due to the dependence structure in the model and approximations are required. Using a variational approximation, we propose a generalized EM algorithm to estimate the parameters of the block mixture model and, to illustrate our approach, we study the case of binary data by using a Bernoulli block mixture.}
|
||
\field{eventtitle}{{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}}
|
||
\field{issn}{1939-3539}
|
||
\field{journaltitle}{IEEE Transactions on Pattern Analysis and Machine Intelligence}
|
||
\field{month}{4}
|
||
\field{number}{4}
|
||
\field{title}{An {{EM}} Algorithm for the Block Mixture Model}
|
||
\field{volume}{27}
|
||
\field{year}{2005}
|
||
\field{dateera}{ce}
|
||
\field{pages}{643\bibrangedash 647}
|
||
\range{pages}{5}
|
||
\verb{doi}
|
||
\verb 10.1109/TPAMI.2005.69
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/6IG45HH2/govaert2005.pdf.pdf;/home/polarolouis/Zotero/storage/TL8M3XRF/Govaert et Nadif - 2005 - An EM algorithm for the block mixture model.pdf;/home/polarolouis/Zotero/storage/2Y48IB26/1401917.html
|
||
\endverb
|
||
\keyw{Approximation algorithms,Classification algorithms,Clustering algorithms,Clustering methods,Data mining,EM algorithm,Index Terms- Block mixture model,Maximum likelihood estimation,Parameter estimation,Partitioning algorithms,Self organizing feature maps,Sparse matrices,variational approximation.}
|
||
\endentry
|
||
\entry{chabert-liddellLearningCommonStructures2023}{online}{}{}
|
||
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||
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||
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||
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||
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|
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{{un=0,uniquepart=base,hash=7fecb6ce38c5ec9d4555962d959d2379}{%
|
||
family={Barbillon},
|
||
familyi={B\bibinitperiod},
|
||
given={Pierre},
|
||
giveni={P\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=06c8f96f3a1aba5140a38275380f781f}{%
|
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family={Donnet},
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||
familyi={D\bibinitperiod},
|
||
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|
||
giveni={S\bibinitperiod},
|
||
givenun=0}}%
|
||
}
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|
||
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||
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|
||
\field{abstract}{Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into sub-collections of structurally homogeneous networks. We tackle these two questions with a probabilistic model based approach. We propose an extension of the Stochastic Block Model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational Expectation-Maximization (EM) algorithm. We derive an ad-hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.}
|
||
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|
||
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|
||
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|
||
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|
||
\field{title}{Learning Common Structures in a Collection of Networks. {{An}} Application to Food Webs}
|
||
\field{type}{article}
|
||
\field{urlday}{22}
|
||
\field{urlmonth}{5}
|
||
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|
||
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|
||
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|
||
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|
||
\verb{doi}
|
||
\verb 10.48550/arXiv.2206.00560
|
||
\endverb
|
||
\verb{eprint}
|
||
\verb 2206.00560
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/M74TXGCF/Chabert-Liddell et al. - 2023 - Learning common structures in a collection of netw.pdf;/home/polarolouis/Zotero/storage/A35M8KNP/2206.html
|
||
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|
||
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|
||
\verb http://arxiv.org/abs/2206.00560
|
||
\endverb
|
||
\verb{url}
|
||
\verb http://arxiv.org/abs/2206.00560
|
||
\endverb
|
||
\keyw{Statistics - Applications,Statistics - Methodology}
|
||
\endentry
|
||
\entry{doreRelativeEffectsAnthropogenic2021}{article}{}{}
|
||
\name{author}{3}{}{%
|
||
{{un=0,uniquepart=base,hash=5f8486271db5e2981426fd924aa1f23e}{%
|
||
family={Doré},
|
||
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|
||
given={Maël},
|
||
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|
||
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|
||
{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
|
||
family={Fontaine},
|
||
familyi={F\bibinitperiod},
|
||
given={Colin},
|
||
giveni={C\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=ced1f9b5102addb42e37cc32bb0822c2}{%
|
||
family={Thébault},
|
||
familyi={T\bibinitperiod},
|
||
given={Elisa},
|
||
giveni={E\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\strng{namehash}{c9a7cb75b065abd1e803419ba2751185}
|
||
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|
||
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|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{Pollinators provide crucial ecosystem services that underpin to wild plant reproduction and yields of insect-pollinated crops. Understanding the relative impacts of anthropogenic pressures and climate on the structure of plant–pollinator interaction networks is vital considering ongoing global change and pollinator decline. Our ability to predict the consequences of global change for pollinator assemblages worldwide requires global syntheses, but these analytical approaches may be hindered by variable methods among studies that either invalidate comparisons or mask biological phenomena. Here we conducted a synthetic analysis that assesses the relative impact of anthropogenic pressures and climatic variability, and accounts for heterogeneity in sampling methodology to reveal network responses at the global scale. We analyzed an extensive dataset, comprising 295 networks over 123 locations all over the world, and reporting over 50,000 interactions between flowering plant species and their insect visitors. Our study revealed that anthropogenic pressures correlate with an increase in generalism in pollination networks while pollinator richness and taxonomic composition are more related to climatic variables with an increase in dipteran pollinator richness associated with cooler temperatures. The contrasting response of species richness and generalism of the plant–pollinator networks stresses the importance of considering interaction network structure alongside diversity in ecological monitoring. In addition, differences in sampling design explained more variation than anthropogenic pressures or climate on both pollination networks richness and generalism, highlighting the crucial need to report and incorporate sampling design in macroecological comparative studies of pollination networks. As a whole, our study reveals a potential human impact on pollination networks at a global scale. However, further research is needed to evaluate potential consequences of loss of specialist species and their unique ecological interactions and evolutionary pathways on the ecosystem pollination function at a global scale.}
|
||
\field{issn}{1365-2486}
|
||
\field{journaltitle}{Global Change Biology}
|
||
\field{langid}{english}
|
||
\field{number}{6}
|
||
\field{title}{Relative Effects of Anthropogenic Pressures, Climate, and Sampling Design on the Structure of Pollination Networks at the Global Scale}
|
||
\field{urlday}{21}
|
||
\field{urlmonth}{6}
|
||
\field{urlyear}{2023}
|
||
\field{volume}{27}
|
||
\field{year}{2021}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\field{pages}{1266\bibrangedash 1280}
|
||
\range{pages}{15}
|
||
\verb{doi}
|
||
\verb 10.1111/gcb.15474
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/89ZXBJQP/10.1111@gcb.15474.pdf.pdf;/home/polarolouis/Zotero/storage/IVR6RGG7/Doré et al. - 2021 - Relative effects of anthropogenic pressures, clima.pdf;/home/polarolouis/Zotero/storage/WSJ4DV98/gcb.html
|
||
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|
||
\verb{urlraw}
|
||
\verb https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474
|
||
\endverb
|
||
\verb{url}
|
||
\verb https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474
|
||
\endverb
|
||
\keyw{anthropogenic pressures,climate,connectance,data,generalism,human impacts,plant-pollinator,pollination networks,richness,sampling effects,specialization}
|
||
\endentry
|
||
\entry{thebaultDatabasePlantpollinatorNetworks2020}{dataset}{}{}
|
||
\name{author}{2}{}{%
|
||
{{un=0,uniquepart=base,hash=ced1f9b5102addb42e37cc32bb0822c2}{%
|
||
family={Thébault},
|
||
familyi={T\bibinitperiod},
|
||
given={Elisa},
|
||
giveni={E\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
|
||
family={Fontaine},
|
||
familyi={F\bibinitperiod},
|
||
given={Colin},
|
||
giveni={C\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\list{publisher}{1}{%
|
||
{Zenodo}%
|
||
}
|
||
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|
||
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|
||
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|
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|
||
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|
||
\field{extradatescope}{labelyear}
|
||
\field{labeldatesource}{}
|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.}
|
||
\field{day}{1}
|
||
\field{month}{12}
|
||
\field{title}{A Database of Plant-Pollinator Networks}
|
||
\field{urlday}{21}
|
||
\field{urlmonth}{6}
|
||
\field{urlyear}{2023}
|
||
\field{version}{1}
|
||
\field{year}{2020}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\verb{doi}
|
||
\verb 10.5281/zenodo.4300427
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb https://zenodo.org/record/4300427
|
||
\endverb
|
||
\verb{url}
|
||
\verb https://zenodo.org/record/4300427
|
||
\endverb
|
||
\keyw{diversity,flower visitors,mutualistic network,plant-pollinator interaction}
|
||
\endentry
|
||
\entry{anakokDisentanglingStructureEcological2022}{online}{}{}
|
||
\name{author}{4}{}{%
|
||
{{un=0,uniquepart=base,hash=fe9e51991c906363fdb9789340eb02b8}{%
|
||
family={Anakok},
|
||
familyi={A\bibinitperiod},
|
||
given={Emre},
|
||
giveni={E\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=7fecb6ce38c5ec9d4555962d959d2379}{%
|
||
family={Barbillon},
|
||
familyi={B\bibinitperiod},
|
||
given={Pierre},
|
||
giveni={P\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=0e7741d31a239c4be11fc31138a136c1}{%
|
||
family={Fontaine},
|
||
familyi={F\bibinitperiod},
|
||
given={Colin},
|
||
giveni={C\bibinitperiod},
|
||
givenun=0}}%
|
||
{{un=0,uniquepart=base,hash=f23dbf986aa073f9022baede42ef0479}{%
|
||
family={Thebault},
|
||
familyi={T\bibinitperiod},
|
||
given={Elisa},
|
||
giveni={E\bibinitperiod},
|
||
givenun=0}}%
|
||
}
|
||
\strng{namehash}{91ac6a753125108a3ce0782cad59a3d8}
|
||
\strng{fullhash}{1f785414b676cf3eff4e0a7230f1c933}
|
||
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|
||
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|
||
\strng{authorbibnamehash}{1f785414b676cf3eff4e0a7230f1c933}
|
||
\strng{authornamehash}{91ac6a753125108a3ce0782cad59a3d8}
|
||
\strng{authorfullhash}{1f785414b676cf3eff4e0a7230f1c933}
|
||
\strng{authorfullhashraw}{1f785414b676cf3eff4e0a7230f1c933}
|
||
\field{sortinit}{7}
|
||
\field{sortinithash}{108d0be1b1bee9773a1173443802c0a3}
|
||
\field{extradatescope}{labelyear}
|
||
\field{labeldatesource}{}
|
||
\true{uniqueprimaryauthor}
|
||
\field{labelnamesource}{author}
|
||
\field{labeltitlesource}{title}
|
||
\field{abstract}{The structure of a bipartite interaction network can be described by providing a clustering for each of the two types of nodes. Such clusterings are outputted by fitting a Latent Block Model (LBM) on an observed network that comes from a sampling of species interactions in the field. However, the sampling is limited and possibly uneven. This may jeopardize the fit of the LBM and then the description of the structure of the network by detecting structures which result from the sampling and not from actual underlying ecological phenomena. If the observed interaction network consists of a weighted bipartite network where the number of observed interactions between two species is available, the sampling efforts for all species can be estimated and used to correct the LBM fit. We propose to combine an observation model that accounts for sampling and an LBM for describing the structure of underlying possible ecological interactions. We develop an original inference procedure for this model, the efficiency of which is demonstrated in simulation studies. The practical interest in ecology of our model is highlighted on a large dataset of plant-pollinator network.}
|
||
\field{day}{29}
|
||
\field{eprintclass}{stat}
|
||
\field{eprinttype}{arxiv}
|
||
\field{langid}{english}
|
||
\field{month}{11}
|
||
\field{title}{Disentangling the Structure of Ecological Bipartite Networks from Observation Processes}
|
||
\field{urlday}{14}
|
||
\field{urlmonth}{6}
|
||
\field{urlyear}{2023}
|
||
\field{year}{2022}
|
||
\field{dateera}{ce}
|
||
\field{urldateera}{ce}
|
||
\verb{eprint}
|
||
\verb 2211.16364
|
||
\endverb
|
||
\verb{file}
|
||
\verb /home/polarolouis/Zotero/storage/LQ3FINZG/Anakok et al. - 2022 - Disentangling the structure of ecological bipartit.pdf
|
||
\endverb
|
||
\verb{urlraw}
|
||
\verb http://arxiv.org/abs/2211.16364
|
||
\endverb
|
||
\verb{url}
|
||
\verb http://arxiv.org/abs/2211.16364
|
||
\endverb
|
||
\keyw{Statistics - Methodology}
|
||
\endentry
|
||
\enddatalist
|
||
\endrefsection
|
||
\endinput
|
||
|