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<h1 class="title">Bilan semaine 8 2026 : 16 février - 20 février</h1>
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<div class="quarto-categories">
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<div class="quarto-category">colBiSBM</div>
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<div class="quarto-category">inférence</div>
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<div class="quarto-category">GNN</div>
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<div class="quarto-title-meta-heading">Auteur·rice</div>
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<div class="quarto-title-meta-heading">Affiliation</div>
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<p class="author">Louis Lacoste <a href="mailto:louis.lacoste@agroparistech.fr" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0009-0004-0178-9821" class="quarto-title-author-orcid"> <img src="data:image/png;base64,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"></a></p>
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<div class="quarto-title-meta-contents">
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<p class="affiliation">
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MIA Paris-Saclay, INRAE, AgroParisTech, Université Paris-Saclay
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<div class="quarto-title-meta-heading">Date de publication</div>
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<p class="date">13 février 2026</p>
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<div class="quarto-title-meta-heading">Modifié</div>
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<p class="date-modified">16 mars 2026</p>
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<h2 id="toc-title">Sur cette page</h2>
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<li><a href="#todo-list" id="toc-todo-list" class="nav-link active" data-scroll-target="#todo-list">TODO List</a>
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<li><a href="#inférence-et-microbes" id="toc-inférence-et-microbes" class="nav-link" data-scroll-target="#inférence-et-microbes">Inférence et microbes</a></li>
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<li><a href="#a-discuter" id="toc-a-discuter" class="nav-link" data-scroll-target="#a-discuter">A discuter</a></li>
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<li><a href="#biblio-à-faire" id="toc-biblio-à-faire" class="nav-link" data-scroll-target="#biblio-à-faire">Biblio à faire</a></li>
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<li><a href="#lectures-en-cours" id="toc-lectures-en-cours" class="nav-link" data-scroll-target="#lectures-en-cours">Lectures en cours 📚</a>
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<li><a href="#hdr-vincent-brault" id="toc-hdr-vincent-brault" class="nav-link" data-scroll-target="#hdr-vincent-brault">HDR Vincent Brault</a></li>
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<li><a href="#ot" id="toc-ot" class="nav-link" data-scroll-target="#ot">OT</a></li>
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<li><a href="#inférence-de-graphes" id="toc-inférence-de-graphes" class="nav-link" data-scroll-target="#inférence-de-graphes">Inférence de graphes</a></li>
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<li><a href="#causalité-1" id="toc-causalité-1" class="nav-link" data-scroll-target="#causalité-1">Causalité</a></li>
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<li><a href="#largest-gaps" id="toc-largest-gaps" class="nav-link" data-scroll-target="#largest-gaps">Largest Gaps</a></li>
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<section id="todo-list" class="level2">
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<h2 class="anchored" data-anchor-id="todo-list">TODO List</h2>
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<ul>
|
||
<li>Petites opérations sur les OTUs (regarder la matrice dans les yeux):
|
||
<ul>
|
||
<li>Ranger les OTUs par variances (i.e. <code>sd(OTU_j)</code>)
|
||
<ul>
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<li>HMC sur-dispersés (au-dessus bissectrice)</li>
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<li>Enterotype phyloseq sous-disp</li>
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</ul></li>
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<li>Regarder la proportion de 1. taxon rares, 2. zeros.</li>
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<li>Faire des coupures selon niveaux taxonomiques et regarder si <span class="math inline">\mathbb{V}_{\text{intra}} \approx \mathbb{V}_{\text{inter}}</span></li>
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<li><em>Bonus</em>: faire ça dans qmd et voir si forge permet gitlab pages</li>
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</ul></li>
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<li>✅ Avec blockmodels, codé un LBM-Séquentiel. <em>Des différences contrastées…</em>
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<ul>
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<li>TODO Ajouter lien vers notebooks résultats</li>
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||
</ul></li>
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||
<li>Relire <span class="citation" data-cites="peixotoHierarchicalBlockStructures2014">Peixoto (<a href="#ref-peixotoHierarchicalBlockStructures2014" role="doc-biblioref">2014</a>)</span>
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<ul>
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<li>Regarder les gens qui citent les travaux de Peixoto</li>
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</ul></li>
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<li>⌛ <strong>En cours</strong> Implémentation <code>blockmodels</code> LBM avec covariables sur proportions (voir <strong>?@eq-modele-covar-prop</strong>)</li>
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||
</ul>
|
||
<div class="callout callout-style-default callout-note callout-titled" title="Idées">
|
||
<div class="callout-header d-flex align-content-center">
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<div class="callout-icon-container">
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||
<i class="callout-icon"></i>
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<div class="callout-title-container flex-fill">
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Idées
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</div>
|
||
</div>
|
||
<div class="callout-body-container callout-body">
|
||
<ul>
|
||
<li>Travailler sur Fungus Tree network</li>
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||
<li>✅ <strong>Demander à PB et SD</strong>, ils regardent : Comparaison covar prop avec GREMLINS multipartite sur (log(dist_phylo), fungus-tree)</li>
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<li>Trouver manière de faire un compromis : <span class="math inline">\ell(Y,Z,W;\theta) - \lambda d(C(W),C_0)</span> avec <span class="math inline">C(W)</span> le clustering seulement sur la base de la structure LBM et <span class="math inline">C_0</span> le clustering de l’arbre. Problème <span class="math inline">d</span> est une distance entre partition, comment optimiser dessus ?</li>
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<li>⌛ Mise à jour partielle des <span class="math inline">\tau</span> : ce qui pose soucis c’est les gros calculs matriciels (c’est vraiment vrai?). Donc sorte de “stochastic” VEM où on update seulement une partie des <span class="math inline">\tau</span> à chaque itération. Et échantillonnage stratifié selon l’arbre ?
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<ul>
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<li>⌛ Simulations avec <span class="math inline">n_2</span> croissant lancée sur Migale</li>
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<li>Réimplementé VE Bernoulli dans colSBM pour Bipartite et début implémentation Stochastic VE. En fait le problème des calculs matriciels <span class="math inline">Y\times(\tau^{(1)})^{\top}</span> (<span class="math inline">n_2^2</span>) donc besoin de sous-échantillonner les noeuds de l’autre dimension à mettre à jour.</li>
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</ul></li>
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</ul>
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</div>
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</div>
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<ul>
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<li><p>Clustering unipartite j’ai cassé une fonction de distance à vérifier et réparer</p></li>
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<li><p>Codes pour le papier :</p>
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<ul>
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<li>Nettoyer les scripts</li>
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<li>Faire un joli README</li>
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<li>❓Faire des notebooks</li>
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</ul></li>
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<li><p>Réussir à reproduire résultat de <span class="citation" data-cites="abramovStructureKnowsBest">Abramov et al. (<a href="#ref-abramovStructureKnowsBest" role="doc-biblioref">s. d.</a>)</span></p></li>
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<li><p>Maitriser graphtools de Peixoto pour essayer d’utiliser l’arbre taxonomique sur graphe de cooccurence inférer par SparCC</p></li>
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<li><p>Maitriser SparCC</p></li>
|
||
<li><p>👶 (délégué à Mona) Clustering sur Doré :</p>
|
||
<ul>
|
||
<li>Ajouter Chao1 et 2, colonne par colonne (site par site), et faire indice moyen et la variance.</li>
|
||
</ul></li>
|
||
</ul>
|
||
<section id="inférence-et-microbes" class="level3">
|
||
<h3 class="anchored" data-anchor-id="inférence-et-microbes">Inférence et microbes</h3>
|
||
<ul>
|
||
<li>✅ En préparation d’un fichier (réu avec JBL à 10h45 le 06/02/2026). Possible en modifiant lbm.h et sbm.h d’obtenir un modèle utilisant les covariables de groupes (de blocs ?). Car besoin de changer <code>membership.m_step()</code> pour mettre à jour <span class="math inline">\pmb\pi</span> et <span class="math inline">\pmb{\rho}</span> en utilisant les <span class="math inline">\pmb B^{\top}\pmb X</span> et en renvoyant l’ELBO adaptée.
|
||
<ul>
|
||
<li><p>😄 Avantage s’inscrit directement dans blockmodels et permet d’avoir toutes les lois d’émissions déjà codées et compatibles !</p></li>
|
||
<li><p>😢 Besoin de réfléchir a une bonne implémentation.</p></li>
|
||
</ul></li>
|
||
</ul>
|
||
<section id="bibliographie-à-lire-à-faire" class="level4">
|
||
<h4 class="anchored" data-anchor-id="bibliographie-à-lire-à-faire">Bibliographie: à lire, à faire</h4>
|
||
<ul>
|
||
<li>Lire article multi-niveaux Saint-Clair</li>
|
||
<li>🆕 🔎 Trouver des papiers:
|
||
<ul>
|
||
<li>LBM Negative Binomial</li>
|
||
<li>Network inference through sample comparison</li>
|
||
</ul></li>
|
||
<li>Idée des groupes sur la base de distance phylogénétique:
|
||
<ul>
|
||
<li>En train de comprendre les distances que phyloseq permet de calculer sur notre exemple</li>
|
||
<li>En train de lire sur Principle coordinate analysis : https://openplantpathology.github.io/OPP_Workshop_Multivariate/2-MV_PCO.html</li>
|
||
<li>Parametric t-SNE pour avoir une unique représentation latente (inconvénient utilise du Deep Learning)</li>
|
||
<li>Lire Papier UniFrac</li>
|
||
</ul></li>
|
||
</ul>
|
||
</section>
|
||
<section id="écrire-et-faire-tourner" class="level4">
|
||
<h4 class="anchored" data-anchor-id="écrire-et-faire-tourner">Écrire et faire tourner</h4>
|
||
<ul>
|
||
<li>🆕 SparCC à différent niveaux</li>
|
||
<li>🆕⌛ Tree-PLN à différents niveaux</li>
|
||
</ul>
|
||
</section>
|
||
<section id="causalité" class="level4">
|
||
<h4 class="anchored" data-anchor-id="causalité">Causalité</h4>
|
||
<p>Plus sur le temps long, à regarder</p>
|
||
<ul>
|
||
<li>GT causalité</li>
|
||
<li>Daria Bystrova lire présentation <span class="citation" data-cites="bystrovaCausalDiscovery">Bystrova (<a href="#ref-bystrovaCausalDiscovery" role="doc-biblioref">s. d.</a>)</span> (Meek rules, V-structure)</li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
</section>
|
||
<section id="a-discuter" class="level2">
|
||
<h2 class="anchored" data-anchor-id="a-discuter">A discuter</h2>
|
||
<ul>
|
||
<li>🆕 Voir pour des Réseaux / GDR ou aller</li>
|
||
<li>🆕 Chercher des cours à suivre</li>
|
||
</ul>
|
||
</section>
|
||
<section id="biblio-à-faire" class="level2">
|
||
<h2 class="anchored" data-anchor-id="biblio-à-faire">Biblio à faire</h2>
|
||
<ul>
|
||
<li>Regarder Transport optimal graphes bipartite.</li>
|
||
</ul>
|
||
</section>
|
||
<section id="lectures-en-cours" class="level2">
|
||
<h2 class="anchored" data-anchor-id="lectures-en-cours">Lectures en cours 📚</h2>
|
||
<section id="hdr-vincent-brault" class="level3">
|
||
<h3 class="anchored" data-anchor-id="hdr-vincent-brault">HDR Vincent Brault</h3>
|
||
<ul>
|
||
<li>⌛ Chap 2 : Creuser l’idée de maximiser l’énergie libre, très intéressant regarder le critère CARI et lire Robert et al 2021. Actuellement p32 du manuscrit</li>
|
||
<li>Chap 3</li>
|
||
</ul>
|
||
</section>
|
||
<section id="ot" class="level3">
|
||
<h3 class="anchored" data-anchor-id="ot">OT</h3>
|
||
<ul>
|
||
<li>⌛ <span class="citation" data-cites="mazeletUnsupervisedLearningOptimal">Mazelet, Flamary, et Thirion (<a href="#ref-mazeletUnsupervisedLearningOptimal" role="doc-biblioref">s. d.</a>)</span> Intéressant pour le transport optimal entre graphes de tailles différentes | Regarder si regularization entropique ne marche pas bien pour le graphe.</li>
|
||
<li>⌛ <span class="citation" data-cites="nennaLecture2Entropic">Nenna (<a href="#ref-nennaLecture2Entropic" role="doc-biblioref">s. d.b</a>)</span> Pour comprendre le problème d’OT régularisé pour l’entropie.</li>
|
||
<li>⌛ <span class="citation" data-cites="nennaLecture1Monge">Nenna (<a href="#ref-nennaLecture1Monge" role="doc-biblioref">s. d.a</a>)</span></li>
|
||
</ul>
|
||
</section>
|
||
<section id="inférence-de-graphes" class="level3">
|
||
<h3 class="anchored" data-anchor-id="inférence-de-graphes">Inférence de graphes</h3>
|
||
<ul>
|
||
<li><p>⌛ <span class="citation" data-cites="aitchisonStatisticalAnalysisCompositional1982a">Aitchison (<a href="#ref-aitchisonStatisticalAnalysisCompositional1982a" role="doc-biblioref">1982</a>)</span>, en cours</p></li>
|
||
<li><p>❗📖 <span class="citation" data-cites="payneFiniteMixturesMultivariate2023">Payne et al. (<a href="#ref-payneFiniteMixturesMultivariate2023" role="doc-biblioref">2023</a>)</span> sur MixMPLN</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="causalité-1" class="level3">
|
||
<h3 class="anchored" data-anchor-id="causalité-1">Causalité</h3>
|
||
<ul>
|
||
<li>❗📖 <span class="citation" data-cites="bystrovaCausalDiscovery">Bystrova (<a href="#ref-bystrovaCausalDiscovery" role="doc-biblioref">s. d.</a>)</span></li>
|
||
</ul>
|
||
</section>
|
||
<section id="largest-gaps" class="level3">
|
||
<h3 class="anchored" data-anchor-id="largest-gaps">Largest Gaps</h3>
|
||
<ul>
|
||
<li>❗📖 <span class="citation" data-cites="braultFastConsistentAlgorithm2023">Brault et Channarond (<a href="#ref-braultFastConsistentAlgorithm2023" role="doc-biblioref">2023</a>)</span></li>
|
||
<li>❗📖 <span class="citation" data-cites="channarondClassificationEstimationStochastic2012">Channarond, Daudin, et Robin (<a href="#ref-channarondClassificationEstimationStochastic2012" role="doc-biblioref">2012</a>)</span> le papier qui introduit le <em>Largest Gaps</em></li>
|
||
</ul>
|
||
|
||
|
||
|
||
</section>
|
||
</section>
|
||
|
||
<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">Les références</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
|
||
<div id="ref-abramovStructureKnowsBest" class="csl-entry" role="listitem">
|
||
Abramov, Kesem, Barry Biton, Geut Galai, Rami Puzis, et Shai Pilosof. s. d. <span>« Structure Knows Best: Predicting Ecological Interactions Across Space Through Pairwise Integration of Latent Network Patterns »</span>.
|
||
</div>
|
||
<div id="ref-aitchisonStatisticalAnalysisCompositional1982a" class="csl-entry" role="listitem">
|
||
Aitchison, J. 1982. <span>« The <span>Statistical Analysis</span> of <span>Compositional Data</span> »</span>. <em>Journal of the Royal Statistical Society. Series B (Methodological)</em> 44 (2): 139‑77. <a href="https://www.jstor.org/stable/2345821">https://www.jstor.org/stable/2345821</a>.
|
||
</div>
|
||
<div id="ref-braultFastConsistentAlgorithm2023" class="csl-entry" role="listitem">
|
||
Brault, Vincent, et Antoine Channarond. 2023. <span>« Fast and <span>Consistent Algorithm</span> for the <span>Latent Block Model</span> »</span>. 9 mars 2023. <a href="https://doi.org/10.48550/arXiv.1610.09005">https://doi.org/10.48550/arXiv.1610.09005</a>.
|
||
</div>
|
||
<div id="ref-bystrovaCausalDiscovery" class="csl-entry" role="listitem">
|
||
Bystrova, Daria. s. d. <span>« Causal Discovery »</span>.
|
||
</div>
|
||
<div id="ref-channarondClassificationEstimationStochastic2012" class="csl-entry" role="listitem">
|
||
Channarond, Antoine, Jean-Jacques Daudin, et Stéphane Robin. 2012. <span>« Classification and Estimation in the <span>Stochastic Blockmodel</span> Based on the Empirical Degrees »</span>. <em>Electronic Journal of Statistics</em> 6 (janvier). <a href="https://doi.org/10.1214/12-ejs753">https://doi.org/10.1214/12-ejs753</a>.
|
||
</div>
|
||
<div id="ref-mazeletUnsupervisedLearningOptimal" class="csl-entry" role="listitem">
|
||
Mazelet, Sonia, Rémi Flamary, et Bertrand Thirion. s. d. <span>« Unsupervised <span>Learning</span> for <span>Optimal Transport</span> Plan Prediction Between Unbalanced Graphs »</span>.
|
||
</div>
|
||
<div id="ref-nennaLecture1Monge" class="csl-entry" role="listitem">
|
||
Nenna, Luca. s. d.a. <span>« Lecture 1 <span>Monge</span> and <span>Kantorovich</span> Problems: From Primal to Dual »</span>.
|
||
</div>
|
||
<div id="ref-nennaLecture2Entropic" class="csl-entry" role="listitem">
|
||
———. s. d.b. <span>« Lecture 2: <span>Entropic Optimal Transport</span> »</span>.
|
||
</div>
|
||
<div id="ref-payneFiniteMixturesMultivariate2023" class="csl-entry" role="listitem">
|
||
Payne, Andrea, Anjali Silva, Steven J. Rothstein, Paul D. McNicholas, et Sanjeena Subedi. 2023. <span>« Finite <span>Mixtures</span> of <span>Multivariate Poisson-Log Normal Factor Analyzers</span> for <span>Clustering Count Data</span> »</span>. 13 novembre 2023. <a href="https://doi.org/10.48550/arXiv.2311.07762">https://doi.org/10.48550/arXiv.2311.07762</a>.
|
||
</div>
|
||
<div id="ref-peixotoHierarchicalBlockStructures2014" class="csl-entry" role="listitem">
|
||
Peixoto, Tiago P. 2014. <span>« Hierarchical <span>Block Structures</span> and <span>High-Resolution Model Selection</span> in <span>Large Networks</span> »</span>. <em>Physical Review X</em> 4 (1): 011047. <a href="https://doi.org/10.1103/PhysRevX.4.011047">https://doi.org/10.1103/PhysRevX.4.011047</a>.
|
||
</div>
|
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if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
|
||
note.children[0].remove();
|
||
}
|
||
const html = processXRef(null, note);
|
||
instance.setContent(html);
|
||
}
|
||
}).finally(() => {
|
||
instance.enable();
|
||
instance.show();
|
||
});
|
||
}
|
||
}, function(instance) {
|
||
});
|
||
}
|
||
let selectedAnnoteEl;
|
||
const selectorForAnnotation = ( cell, annotation) => {
|
||
let cellAttr = 'data-code-cell="' + cell + '"';
|
||
let lineAttr = 'data-code-annotation="' + annotation + '"';
|
||
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
|
||
return selector;
|
||
}
|
||
const selectCodeLines = (annoteEl) => {
|
||
const doc = window.document;
|
||
const targetCell = annoteEl.getAttribute("data-target-cell");
|
||
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
|
||
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
|
||
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
|
||
const lineIds = lines.map((line) => {
|
||
return targetCell + "-" + line;
|
||
})
|
||
let top = null;
|
||
let height = null;
|
||
let parent = null;
|
||
if (lineIds.length > 0) {
|
||
//compute the position of the single el (top and bottom and make a div)
|
||
const el = window.document.getElementById(lineIds[0]);
|
||
top = el.offsetTop;
|
||
height = el.offsetHeight;
|
||
parent = el.parentElement.parentElement;
|
||
if (lineIds.length > 1) {
|
||
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
|
||
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
|
||
height = bottom - top;
|
||
}
|
||
if (top !== null && height !== null && parent !== null) {
|
||
// cook up a div (if necessary) and position it
|
||
let div = window.document.getElementById("code-annotation-line-highlight");
|
||
if (div === null) {
|
||
div = window.document.createElement("div");
|
||
div.setAttribute("id", "code-annotation-line-highlight");
|
||
div.style.position = 'absolute';
|
||
parent.appendChild(div);
|
||
}
|
||
div.style.top = top - 2 + "px";
|
||
div.style.height = height + 4 + "px";
|
||
div.style.left = 0;
|
||
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
|
||
if (gutterDiv === null) {
|
||
gutterDiv = window.document.createElement("div");
|
||
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
|
||
gutterDiv.style.position = 'absolute';
|
||
const codeCell = window.document.getElementById(targetCell);
|
||
const gutter = codeCell.querySelector('.code-annotation-gutter');
|
||
gutter.appendChild(gutterDiv);
|
||
}
|
||
gutterDiv.style.top = top - 2 + "px";
|
||
gutterDiv.style.height = height + 4 + "px";
|
||
}
|
||
selectedAnnoteEl = annoteEl;
|
||
}
|
||
};
|
||
const unselectCodeLines = () => {
|
||
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
|
||
elementsIds.forEach((elId) => {
|
||
const div = window.document.getElementById(elId);
|
||
if (div) {
|
||
div.remove();
|
||
}
|
||
});
|
||
selectedAnnoteEl = undefined;
|
||
};
|
||
// Handle positioning of the toggle
|
||
window.addEventListener(
|
||
"resize",
|
||
throttle(() => {
|
||
elRect = undefined;
|
||
if (selectedAnnoteEl) {
|
||
selectCodeLines(selectedAnnoteEl);
|
||
}
|
||
}, 10)
|
||
);
|
||
function throttle(fn, ms) {
|
||
let throttle = false;
|
||
let timer;
|
||
return (...args) => {
|
||
if(!throttle) { // first call gets through
|
||
fn.apply(this, args);
|
||
throttle = true;
|
||
} else { // all the others get throttled
|
||
if(timer) clearTimeout(timer); // cancel #2
|
||
timer = setTimeout(() => {
|
||
fn.apply(this, args);
|
||
timer = throttle = false;
|
||
}, ms);
|
||
}
|
||
};
|
||
}
|
||
// Attach click handler to the DT
|
||
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
|
||
for (const annoteDlNode of annoteDls) {
|
||
annoteDlNode.addEventListener('click', (event) => {
|
||
const clickedEl = event.target;
|
||
if (clickedEl !== selectedAnnoteEl) {
|
||
unselectCodeLines();
|
||
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
|
||
if (activeEl) {
|
||
activeEl.classList.remove('code-annotation-active');
|
||
}
|
||
selectCodeLines(clickedEl);
|
||
clickedEl.classList.add('code-annotation-active');
|
||
} else {
|
||
// Unselect the line
|
||
unselectCodeLines();
|
||
clickedEl.classList.remove('code-annotation-active');
|
||
}
|
||
});
|
||
}
|
||
const findCites = (el) => {
|
||
const parentEl = el.parentElement;
|
||
if (parentEl) {
|
||
const cites = parentEl.dataset.cites;
|
||
if (cites) {
|
||
return {
|
||
el,
|
||
cites: cites.split(' ')
|
||
};
|
||
} else {
|
||
return findCites(el.parentElement)
|
||
}
|
||
} else {
|
||
return undefined;
|
||
}
|
||
};
|
||
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
|
||
for (var i=0; i<bibliorefs.length; i++) {
|
||
const ref = bibliorefs[i];
|
||
const citeInfo = findCites(ref);
|
||
if (citeInfo) {
|
||
tippyHover(citeInfo.el, function() {
|
||
var popup = window.document.createElement('div');
|
||
citeInfo.cites.forEach(function(cite) {
|
||
var citeDiv = window.document.createElement('div');
|
||
citeDiv.classList.add('hanging-indent');
|
||
citeDiv.classList.add('csl-entry');
|
||
var biblioDiv = window.document.getElementById('ref-' + cite);
|
||
if (biblioDiv) {
|
||
citeDiv.innerHTML = biblioDiv.innerHTML;
|
||
}
|
||
popup.appendChild(citeDiv);
|
||
});
|
||
return popup.innerHTML;
|
||
});
|
||
}
|
||
}
|
||
});
|
||
</script>
|
||
</div> <!-- /content -->
|
||
|
||
|
||
|
||
|
||
</body></html> |