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Plutniak maisonobe resto atelier2-network

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L'analyse de graphe avec R

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Plutniak maisonobe resto atelier2-network

  1. 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R M͐ʼnŇĽŅŊĹň, ķŀŊňʼnĹŇĽłĻ, ŋĽňŊĵŀĽňĵʼnĽŃł Sébastien Plutniak1 Marion Maisonobe2 1Lisst-Cers, Ehess — 2Lisst-Cieu, Labex SMS ǨǬ mai ǩǧǨǫ ResTO, Toulouse
  2. 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R IłʼnŇŃĸŊķʼnĽŃł D͐ŇŃŊŀ͐ ĸĹ ŀ’ĵʼnĹŀĽĹŇ D͐ŇŃŊŀ͐ ĸĹ ŀ’ĵʼnĹŀĽĹŇ lj IłʼnŇŃĸŊķʼnĽŃł Déroulé de l’atelier Tour de table NJ PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R Les graphes, objets mathématiques et R Les package R concernant l’analyse de graphes Ressources en ligne Nj MĵłĽńŊŀĹŇ ĵŋĹķ IĽʼnķņľ Ĺʼn ŋńĻŋ nj Uł ĹŎĹŁńŀĹ ĸĹ ńŇŃľĹʼn
  3. 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R IłʼnŇŃĸŊķʼnĽŃł TŃŊŇ ĸĹ ʼnĵĶŀĹ TŃŊŇ ĸĹ ʼnĵĶŀĹ Pour commencer… Types de données relationnelles que chacun a à traiter ? Quels outils déjà utilisés ? Leurs limites éventuelles ? En conséquence, quels besoins ? (Quelle connaissance préalable de R ? )
  4. 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R LĹň ĻŇĵńļĹň, ŃĶľĹʼnň Łĵʼnļ͐ŁĵʼnĽŅŊĹň Ĺʼn R LĹň ĻŇĵńļĹň, ŃĶľĹʼnň Łĵʼnļ͐ŁĵʼnĽŅŊĹň Ĺʼn R Le graphe comme objet mathématique Une graphe est composé : d’un ensemble d’éléments qui sont les sommets (ou noeuds) du graphe ; et d’un ensemble d’éléments qui sont les arètes (ou arcs) du graphe. Les arètes peuvent être orientées ou non. Implémentation minimale dans R un objet data.frame contenant une liste d’arètes ; un objet matrix contenant une matrice carrée des (id des) noeuds en colonne, et la valeur des liens dans les cases (Ǩ ou ǧ pour les graphes non valués ; une valeur pour les graphes valués).
  5. 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň Ǩǩǫ packages ayant le mot « network » dans leur titre ou leur description sur CRAN abn Data Modelling with Additive Bayesian Networks amen Additive and multiplicative effects modeling of networks and relational data AMORE A MORE flexible neural network package ANN Feedforward Artificial Neural Network optimized by Genetic Algorithm ARTIVA Infer a time-varying DBN network from time series data BiomarkeR Paired (pBI) and Unpaired Biomarker Identifier (uBI) including a method to infer networks bionetdata Biological and chemical data networks bioPN Simulation of deterministic and stochastic biochemical reaction networks using Petri Nets bipartite Visualising bipartite networks and calculating some (ecological) indices blkergm Fitting block ERGM given the block structure on social networks blockmodeling An R package for Generalized and classical blockmodeling of valued networks BMN The pseudo-likelihood method for pairwise binary markov networks bnlearn Bayesian network structure learning, parameter learning and inference BoolNet Generation, reconstruction, simulation and analysis of synchronous, asynchronous, and probabilistic Boolean networks brnn brnn (Bayesian regularization for feed-forward neural networks) cǪnet Infering large-scale gene networks with CǪNET CaDENCE Conditional Density Estimation Network Construction and Evaluation catnet Categorical Bayesian Network Inference CCMnet Simulate Congruence Class Model for Networks CHCN Canadian Historical Climate Network CIDnetworks Generative models for networks with conditionally independent dyadic structure condmixt Conditional Density Estimation with Neural Network Conditional Mixtures COSINE COndition SpecIfic sub-NEtwork crn Downloads and Builds datasets for Climate Reference Network dǪNetwork Tools for creating DǪ JavaScript network and tree graphs from R
  6. 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň Ǩǩǫ packages ayant le mot « network » dans leur titre ou leur description sur CRAN ddepn Dynamic Deterministic Effects Propagation Networks : Infer signalling networks for timecourse RPPA data deal Learning Bayesian Networks with Mixed Variables degreenet Models for Skewed Count Distributions Relevant to Networks diagram Functions for visualising simple graphs (networks), plotting flow diagrams dils Data-Informed Link Strength. Combine multiple-relationship networks into a single weighted network. Impute (fill-in) missing network links dna Differential Network Analysis dnet Integrative analysis of digitised data in terms of network, ontology and evolution Dominance ADI (average dominance index), social network graphs with dual directions, and music notation graph dvn Access to The Dataverse Network APIs ebdbNet Empirical Bayes Estimation of Dynamic Bayesian Networks EDISON SoƜware for network reconstruction and changepoint detection egonet Tool for ego-centric measures in Social Network Analysis elmNN Implementation of ELM (Extreme Learning Machine ) algorithm for SLFN ( Single Hidden Layer Feedforward Neural Networks ) ENA Ensemble Network Aggregation enaR Tools for ecological network analysis (ena) in R epinet A collection of epidemic/network-related tools ergm Fit, Simulate and Diagnose Exponential-Family Models for Networks ergm.count Fit, Simulate and Diagnose Exponential-Family Models for Networks with Count Edges ergmharris Local Health Department network data set foodweb visualisation and analysis of food web networks GǨDBN A package performing Dynamic Bayesian Network inference GANPA Gene Association Network-based Pathway Analysis gemtc GeMTC network meta-analysis GeneNet Modeling and Inferring Gene Networks GeneReg Construct time delay gene regulatory network
  7. 7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň Ǩǩǫ packages ayant le mot « network » dans leur titre ou leur description sur CRAN geospt Spatial geostatistics ; some geostatistical and radial basis functions, prediction and cross validation ; design of optimal spatial sampling networks based on GEVcdn GEV conditional density estimation network GOGANPA GO-Functional-Network-based Gene-Set-Analysis gRain Graphical Independence Networks grnn General regression neural network igraph Network analysis and visualization igraphdata A collection of network data sets for the igraph package InteractiveIGraph interactive network analysis and visualization intergraph Coercion routines for network data objects in R interventionalDBN Interventional Inference for Dynamic Bayesian Networks latentnet Latent position and cluster models for statistical networks linkcomm Tools for Generating, Visualizing, and Analysing Link Communities in Networks LogitNet Infer network based on binary arrays using regularized logistic regression loop loop decomposition of weighted directed graphs for life cycle analysis, providing flexbile network plotting methods, and analyzing food chain properties in mlDNA Machine Learning-based Differential Network Analysis of Transcriptome Data monmlp Monotone multi-layer perceptron neural network MPINet The package can implement the network-based metabolite pathway identification of pathways mugnet Mixture of Gaussian Bayesian Network Model multiplex Analysis of Multiple Social Networks with Algebra ndtv Network Dynamic Temporal Visualizations netClass netClass : An R Package for Network-Based Biomarker Discovery NetCluster Clustering for networks NetComp Network Generation and Comparison NetData Network Data for McFarland’s SNA R labs NetIndices Estimating network indices, including trophic structure of foodwebs in R
  8. 8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň Ǩǩǫ packages ayant le mot « network » dans leur titre ou leur description sur CRAN netmeta Network meta-analysis with R NetPreProc NetPreProc : Network Pre-Processing and normalization nets Network Estimation for Time Series NetSim A Social Networks Simulation Tool in R netweavers NetWeAvers : Weighted Averages for Networks network Classes for Relational Data networkDynamic Dynamic Extensions for Network Objects networkDynamicData dynamic network datasets networkreporting Tools for using network reporting estimators networksis Simulate bipartite graphs with fixed marginals through sequential importance sampling networkTomography Tools for network tomography neuralnet Training of neural networks nnet Feed-forward Neural Networks and Multinomial Log-Linear Models nws R functions for NetWorkSpaces and Sleigh parmigene Parallel Mutual Information estimation for Gene Network reconstruction pcnetmeta Methods for patient-centered network meta-analysis pnn Probabilistic neural networks qgraph Network representations of relationships in data qrnn Quantile regression neural network qtlnet Causal Inference of QTL Networks QuACN QuACN : Quantitative Analysis of Complex Networks queueing Analysis of Queueing Networks and Models rbmn Handling Linear Gaussian Bayesian Networks RCurl General network (HTTP/FTP/...) client interface for R rDNA R Bindings for the Discourse Network Analyzer
  9. 9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň Ǩǩǫ packages ayant le mot « network » dans leur titre ou leur description sur CRAN ResistorArray electrical properties of resistor networks RSiena Siena - Simulation Investigation for Empirical Network Analysis RSNNS Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS) sand Statistical Analysis of Network Data with R sbioPN sbioPN : Simulation of deterministic and stochastic spatial biochemical reaction networks using Petri Nets sdnet SoƜ Discretization-based Bayesian Network Inference SIMMS Subnetwork Integration for Multi-Modal Signatures simone Statistical Inference for MOdular NEtworks (SIMoNe) sna Tools for Social Network Analysis SNFtool Similarity Network Fusion snow Simple Network of Workstations snowFT Fault Tolerant Simple Network of Workstations SocialNetworks Generates social networks based on distance SSN Spatial Modeling on Stream Networks statnet SoƜware tools for the Statistical Analysis of Network Data SyNet Inference and Analysis of Sympatry Networks TeachNet Fits neural networks to learn about back propagation tergm Fit, Simulate and Diagnose Models for Network Evolution based on Exponential-Family Random Graph Models timeordered Time-ordered and time-aggregated network analyses tnet tnet : SoƜware for Analysis of Weighted, Two-mode, and Longitudinal networks transnet Conducts transmission modeling on a bayesian network VBLPCM Variational Bayes Latent Position Cluster Model for networks wccsom SOM networks for comparing patterns with peak shiƜs WGCNA Weighted Correlation Network Analysis
  10. 10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň Les packages généralistes Statnet/network : anciennement sna ; développé par Carter Butts (univ. de Californie). Particulièrement bien fourni pour la modélisation ; Igraph : développé par Gabor Csardi (univ. de Budapest). Davantage d’indicateurs et de métriques — disponible sous R, Python et C ; le package intergraph permet des conversions d’objets network <> igraph.
  11. 11. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň LĹň ńĵķĿĵĻĹ R ķŃłķĹŇłĵłʼn ŀ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň Packages spécialisés gplot : visualisation de graphes produits avec statnet ; bipartite : analyse de réseaux bipartis ; tnet : analyse de réseaux valués ; egonet : extraction et analyse de réseau égocentrés ; ndtv : visualisation dynamique de réseaux igraph (produit des .gif).
  12. 12. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R RĹňňŃŊŇķĹň Ĺł ŀĽĻłĹ RĹňňŃŊŇķĹň ĸĹ ĺŃŇŁĵʼnĽŃł Ĺł ŀĽĻłĹ Tutoriels (en français) Un présentation générale, basée sur statnet : Barnier J. ǩǧǨǨ, Analyse de réseaux avec R, http://alea.fr.eu.org/. Un tutoriel pas-à-pas plus avancé, présentant plusieurs packages : Beauguitte L. ǩǧǨǨ, Analyser les réseaux avec R (packages statnet, igraph et tnet), FMR.
  13. 13. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R RĹňňŃŊŇķĹň Ĺł ŀĽĻłĹ RĹňňŃŊŇķĹň ĸĹ ĺŃŇŁĵʼnĽŃł Ĺł ŀĽĻłĹ Articles dans le R Journal http://journal.r-project.org/ Hankin ǩǧǧǭ, “Electrical properties of resistor networks”. R News, ǭ(ǩ) : Ǭǩ-ǬǪ. Long & Carey ǩǧǧǭ, “Graphs and networks : Tools in Bioconductor”. R News, ǭ(Ǭ) : ǩ–Ǯ. Schäfer, Opgen-Rhein & Strimmer ǩǧǧǭ, “Reverse engineering genetic networks using the GeneNet package”. R News, ǭ(Ǭ) : Ǭǧ–ǬǪ. Dormann, Gruber & Fründ ǩǧǧǯ, “Introducing the bipartite package : Analysing ecological networks”. R News, ǯ(ǩ) : ǯ–ǨǨ. Articles dans le J. of Statistical SoƜware http://www.jstatsoft.org Butts & Carter ǩǧǧǯ, “Social network analysis with sna”. Journal of Statistical SoƜware, ǩǫ(ǭ) : Ǩ–ǬǨ. Butts & Carter ǩǧǧǯ, “network : A Package for Managing Relational Data in R”. Journal of Statistical SoƜware, ǩǫ(ǩ) : Ǩ–Ǫǭ. Bender-deMoll, Morris & Moody ǩǧǧǯ, ”Prototype Packages for Managing and Animating Longitudinal Network Data : dynamicnetwork and rSoNIA”. Journal of Statistical SoƜware, ǩǫ(Ǯ).
  14. 14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R PĵłŃŇĵŁĵ ĸĹň ńŃňňĽĶĽŀĽʼn͐ň ĸ’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹ ňŃŊň R RĹňňŃŊŇķĹň Ĺł ŀĽĻłĹ RĹňňŃŊŇķĹň ĸĹ ĺŃŇŁĵʼnĽŃł Ĺł ŀĽĻłĹ Sites internet Le site de statnet/sna : http://statnet.csde.washington.edu/ Le site de igraph : http://igraph.sourceforge.net/ Le groupe « Flux, matrices, réseaux » (FMR) : http://groupefmr.hypotheses.org/ Le site de Tore Opsahl, développeur de tnet : http://toreopsahl.com/ Le site de Julien Barnier, développeur de rgrs/questionr : http://alea.fr.eu.org/ Le site de l’International Network for Social Network Analysis (Sunbelt Social Networks Conference) : http://www.insna.org/
  15. 15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R MĵłĽńŊŀĹŇ ĵŋĹķ IĽʼnķņľ Ĺʼn ŋńĻŋ MĵłĽńŊŀĹŇ ĵŋĹķ IĽʼnķņľ Ĺʼn ŋńĻŋ Maintenant, quelques manipulations.
  16. 16. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R Uł ĹŎĹŁńŀĹ ĸĹ ńŇŃľĹʼn Uł ĹŎĹŁńŀĹ ĸĹ ńŇŃľĹʼn Une étude de réception d’un ensemble d’articles scientifiques…
  17. 17. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L’ĵłĵŀŏňĹ ĸĹ ĻŇĵńļĹň ĵŋĹķ R Uł ĹŎĹŁńŀĹ ĸĹ ńŇŃľĹʼn Questions, discussions… ? marion.maisonobe@univ-tlse2.fr sebastien.plutniak@ehess.fr

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