diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json index 8504b19..65b5590 100644 --- a/.obsidian/workspace.json +++ b/.obsidian/workspace.json @@ -270,20 +270,20 @@ }, "active": "bd694dfa434e6a04", "lastOpenFiles": [ + "Résumé des tâches.md", + "Thèse/Résolution des problèmes/Problème avec renv.md", "Thèse/Packages/R/colSBM.md", "Thèse/Articles/Review papier colBiSBM.md", "Thèse/Packages/R", "Thèse/Packages", "Thèse/Projets annexes/Application colBiSBM réseaux d'optimisation de NN.md", "Thèse/Projets annexes/Applications colBiSBM pour impact pratiques agris sur interactions plantes pollinisateurs.md", - "Résumé des tâches.md", "Thèse/TODO/2026-05-18.md", "macros.tex.md", "Thèse/Lectures/local_macros.tex.md", "Thèse/Projets annexes/VGAE avec (Gromov-)Wasserstein.md", "Thèse/Projets annexes", "Thèse/Axes/Phylogénie/SBM avec covariance latente.md", - "Thèse/Résolution des problèmes/Problème avec renv.md", "Thèse/Lectures/@quericBridgingMaximumLikelihood2026.md", "@quericBridgingMaximumLikelihood2026.md", "Thèse/Résumés séminaires.md", diff --git a/Thèse/these_ref.bib b/Thèse/these_ref.bib index 1b42024..1a60895 100644 --- a/Thèse/these_ref.bib +++ b/Thèse/these_ref.bib @@ -309,6 +309,24 @@ Read\_Status\_Date: 2025-11-03T12:31:15.098Z}, file = {/home/louis/snap/zotero-snap/common/Zotero/storage/YI2QU6FD/Barbillon - Sciences des données apprentissage statistique.pdf} } +@article{bascompteNestedAssemblyPlant2003, + title = {The Nested Assembly of Plant--Animal Mutualistic Networks}, + author = {Bascompte, Jordi and Jordano, Pedro and Meli{\'a}n, Carlos J. and Olesen, Jens M.}, + year = 2003, + month = aug, + journal = {Proceedings of the National Academy of Sciences}, + volume = {100}, + number = {16}, + pages = {9383--9387}, + publisher = {Proceedings of the National Academy of Sciences}, + doi = {10.1073/pnas.1633576100}, + urldate = {2026-06-10}, + abstract = {Most studies of plant--animal mutualisms involve a small number of species. There is almost no information on the structural organization of species-rich mutualistic networks despite its potential importance for the maintenance of diversity. Here we analyze 52 mutualistic networks and show that they are highly nested; that is, the more specialist species interact only with proper subsets of those species interacting with the more generalists. This assembly pattern generates highly asymmetrical interactions and organizes the community cohesively around a central core of interactions. Thus, mutualistic networks are neither randomly assembled nor organized in compartments arising from tight, parallel specialization. Furthermore, nestedness increases with the complexity (number of interactions) of the network: for a given number of species, communities with more interactions are significantly more nested. Our results indicate a nonrandom pattern of community organization that may be relevant for our understanding of the organization and persistence of biodiversity.}, + annotation = {Read\_Status: New\\ +Read\_Status\_Date: 2026-06-10T15:10:14.000Z}, + file = {/home/louis/snap/zotero-snap/common/Zotero/storage/MHN9EMQR/Bascompte et al. - 2003 - The nested assembly of plant–animal mutualistic networks.pdf} +} + @article{bashanUniversalityHumanMicrobial2016a, title = {Universality of {{Human Microbial Dynamics}}}, author = {Bashan, Amir and Gibson, Travis E. and Friedman, Jonathan and Carey, Vincent J. and Weiss, Scott T. and Hohmann, Elizabeth L. and Liu, Yang-Yu}, @@ -715,6 +733,26 @@ Read\_Status\_Date: 2025-09-26T08:52:07.522Z}, file = {/home/louis/snap/zotero-snap/common/Zotero/storage/6XFLLTKL/Chabert-Liddell et al. - 2021 - A Stochastic Block Model Approach for the Analysis of Multilevel Networks an Application to the Soc.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/8DAMTVD8/1910.html} } +@article{chacoffInteractionFrequencyNetwork2018, + title = {Interaction Frequency, Network Position, and the Temporal Persistence of Interactions in a Plant--Pollinator Network}, + author = {Chacoff, Natacha P. and Resasco, Julian and V{\'a}zquez, Diego P.}, + year = 2018, + journal = {Ecology}, + volume = {99}, + number = {1}, + pages = {21--28}, + issn = {1939-9170}, + doi = {10.1002/ecy.2063}, + urldate = {2026-06-10}, + abstract = {Ecological interactions are highly dynamic in time and space. Previous studies of plant--animal mutualistic networks have shown that the occurrence of interactions varies substantially across years. We analyzed interannual variation of a quantitative mutualistic network, in which links are weighted by interaction frequency. The network was sampled over six consecutive years, representing one of the longest time series for a community-wide mutualistic network. We estimated the interannual similarity in interactions and assessed the determinants of their persistence. The occurrence of interactions varied greatly among years, with most interactions seen in only one year (64\%) and few (20\%) in more than two years. This variation was associated with the frequency and position of interactions relative to the network core, so that the network consisted of a persistent core of frequent interactions and many peripheral, infrequent interactions. Null model analyses suggest that species abundances play a substantial role in generating these patterns. Our study represents an important step in the study of ecological networks, furthering our mechanistic understanding of the ecological processes driving the temporal persistence of interactions.}, + copyright = {\copyright{} 2017 by the Ecological Society of America}, + langid = {english}, + keywords = {/unread,interaction frequency,Monte Desert,nestedness,network core,network dynamics,null model,sampling artifacts,species abundance,temporal variability}, + annotation = {Read\_Status: New\\ +Read\_Status\_Date: 2026-06-10T15:30:17.010Z}, + file = {/home/louis/snap/zotero-snap/common/Zotero/storage/33QVBXTA/Chacoff et al. - 2018 - Interaction frequency, network position, and the temporal persistence of interactions in a plant–pol.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/JV67XEWD/ecy.html} +} + @article{chaffronCommunityNetworkModels, title = {Community Network Models to Reveal Marine Plankton Systems Ecology and Evolution}, author = {Chaffron, Samuel}, @@ -974,6 +1012,27 @@ Read\_Status\_Date: 2026-05-05T09:10:00.661Z}, file = {/home/louis/snap/zotero-snap/common/Zotero/storage/UP2URTE2/Delon et Desolneux - 2020 - A Wasserstein-type distance in the space of Gaussi.pdf} } +@article{demanincorDoesPhenologyExplain2020, + title = {Does Phenology Explain Plant--Pollinator Interactions at Different Latitudes? {{An}} Assessment of Its Explanatory Power in Plant--Hoverfly Networks in {{French}} Calcareous Grasslands}, + shorttitle = {Does Phenology Explain Plant--Pollinator Interactions at Different Latitudes?}, + author = {{de Manincor}, Natasha and Hautekeete, Nina and Piquot, Yves and Schatz, Bertrand and Vanappelghem, C{\'e}dric and Massol, Fran{\c c}ois}, + year = 2020, + journal = {Oikos}, + volume = {129}, + number = {5}, + pages = {753--765}, + issn = {1600-0706}, + doi = {10.1111/oik.07259}, + urldate = {2026-06-10}, + abstract = {For plant--pollinator interactions to occur, the flowering of plants and the flying period of pollinators (i.e. their phenologies) have to overlap. Yet, few models make use of this principle to predict interactions and fewer still are able to compare interaction networks of different sizes. Here, we tackled both challenges using Bayesian structural equation models (SEM), incorporating the effect of phenological overlap in six plant--hoverfly networks. Insect and plant abundances were strong determinants of the number of visits, while phenology overlap alone was not sufficient, but significantly improved model fit. Phenology overlap was a stronger determinant of plant--pollinator interactions in sites where the average overlap was longer and network compartmentalization was weaker, i.e. at higher latitudes. Our approach highlights the advantages of using Bayesian SEMs to compare interaction networks of different sizes along environmental gradients and articulates the various steps needed to do so.}, + copyright = {\copyright{} 2020 Nordic Society Oikos. Published by John Wiley \& Sons Ltd}, + langid = {english}, + keywords = {/unread,Bayesian model,interaction probability,latent block model,latitudinal gradient,mutualistic network,phenology overlap,species abundance,structural equation model}, + annotation = {Read\_Status: New\\ +Read\_Status\_Date: 2026-06-10T15:43:45.787Z}, + file = {/home/louis/snap/zotero-snap/common/Zotero/storage/74LCWZIQ/de Manincor et al. - 2020 - Does phenology explain plant–pollinator interactions at different latitudes An assessment of its ex.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/55RVRYKD/oik.html} +} + @article{dempsterMaximumLikelihoodIncomplete1977, title = {Maximum {{Likelihood}} from {{Incomplete Data}} via the {{EM Algorithm}}}, author = {Dempster, A. P. and Laird, N. M. and Rubin, D. B.}, @@ -1533,6 +1592,25 @@ Read\_Status\_Date: 2025-05-07T07:43:04.957Z} file = {/home/louis/snap/zotero-snap/common/Zotero/storage/YIUG7VAU/Hamilton et al. - Inductive Representation Learning on Large Graphs.pdf} } +@misc{hamiltonInductiveRepresentationLearning2018, + title = {Inductive {{Representation Learning}} on {{Large Graphs}}}, + author = {Hamilton, William L. and Ying, Rex and Leskovec, Jure}, + year = 2018, + month = sep, + number = {arXiv:1706.02216}, + eprint = {1706.02216}, + primaryclass = {cs.SI}, + publisher = {arXiv}, + doi = {10.48550/arXiv.1706.02216}, + urldate = {2026-06-10}, + abstract = {Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.}, + archiveprefix = {arXiv}, + keywords = {/unread,Computer Science - Machine Learning,Computer Science - Social and Information Networks,Statistics - Machine Learning}, + annotation = {Read\_Status: New\\ +Read\_Status\_Date: 2026-06-10T15:49:15.404Z}, + file = {/home/louis/snap/zotero-snap/common/Zotero/storage/NPG9W4DV/Hamilton et al. - 2018 - Inductive Representation Learning on Large Graphs.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/W85VX37U/1706.html} +} + @article{hendersonDerivingInverseSum1981, title = {On {{Deriving}} the {{Inverse}} of a {{Sum}} of {{Matrices}}}, author = {Henderson, H. V. and Searle, S. R.}, @@ -2194,6 +2272,46 @@ Read\_Status\_Date: 2025-06-11T09:08:09.864Z}, file = {/home/louis/snap/zotero-snap/common/Zotero/storage/4UCENIQQ/Michalska-Smith et Allesina - 2019 - Telling ecological networks apart by their structure A computational challenge.pdf} } +@article{mieleCorePeripheryDynamics2020, + title = {Core--Periphery Dynamics in a Plant--Pollinator Network}, + author = {Miele, Vincent and {Ramos-Jiliberto}, Rodrigo and V{\'a}zquez, Diego P.}, + year = 2020, + journal = {Journal of Animal Ecology}, + volume = {89}, + number = {7}, + pages = {1670--1677}, + issn = {1365-2656}, + doi = {10.1111/1365-2656.13217}, + urldate = {2026-06-10}, + abstract = {Mutualistic networks are highly dynamic, characterized by high temporal turnover of species and interactions. Yet, we have a limited understanding of how the internal structure of these networks and the roles species play in them vary through time. We used 6 years of observation data and a novel statistical method (dynamic stochastic block models) to assess how network structure and species' structural position within the network change throughout subseasons of the flowering season and across years in a quantitative plant--pollinator network from a dryland ecosystem in Argentina. Our analyses revealed a core--periphery structure persistent through subseasons and years. Yet, species structural position as core or peripheral was highly dynamic: virtually all species that were at the core in some subseasons were also peripheral in other subseasons, while many other species always remained peripheral. Our results illuminate our understanding of the dynamics of mutualistic networks and have important implications for ecosystem management and conservation.}, + copyright = {\copyright{} 2020 The Authors. Journal of Animal Ecology published by John Wiley \& Sons Ltd on behalf of British Ecological Society}, + langid = {english}, + keywords = {/unread,core-periphery structure,mutualistic networks,plant-pollinator interactions,species role,stochastic block model,temporal dynamics}, + annotation = {Read\_Status: New\\ +Read\_Status\_Date: 2026-06-10T15:31:07.188Z}, + file = {/home/louis/snap/zotero-snap/common/Zotero/storage/WAJ49JQA/Miele et al. - 2020 - Core–periphery dynamics in a plant–pollinator network.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/TPH4YZSH/1365-2656.html} +} + +@article{mieleNineQuickTips2019, + title = {Nine Quick Tips for Analyzing Network Data}, + author = {Miele, Vincent and Matias, Catherine and Robin, St{\'e}phane and Dray, St{\'e}phane}, + year = 2019, + month = dec, + journal = {PLOS Computational Biology}, + volume = {15}, + number = {12}, + pages = {e1007434}, + publisher = {Public Library of Science}, + issn = {1553-7358}, + doi = {10.1371/journal.pcbi.1007434}, + urldate = {2026-06-10}, + langid = {english}, + keywords = {/unread,Biologists,Food web structure,Genetic networks,Mathematical models,Network analysis,Neural networks,Protein interaction networks,Software tools}, + annotation = {Read\_Status: New\\ +Read\_Status\_Date: 2026-06-10T16:02:33.829Z}, + file = {/home/louis/snap/zotero-snap/common/Zotero/storage/GWZ8PNZB/Miele et al. - 2019 - Nine quick tips for analyzing network data.pdf} +} + @incollection{MonotheticAnalysisProgram1990, title = {Monothetic {{Analysis}} ({{Program MONA}})}, booktitle = {Finding {{Groups}} in {{Data}}},