Plutniak maisonobe resto atelier2-network

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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

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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

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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 ? )

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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).

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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

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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

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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

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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

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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

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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.

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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).

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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.

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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).

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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/

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MĵłĽńŊŀĹŇ ĵŋĹķ IĽŉķņľ Ĺŉ ŋńĻŋ
MĵłĽńŊŀĹŇ ĵŋĹķ IĽŉķņľ Ĺŉ ŋńĻŋ
Maintenant, quelques manipulations.

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Uł ĹŎĹŁńŀĹ ĸĹ ńŇŃľĹŉ
Une étude de réception d’un ensemble d’articles scientifiques…

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Questions, discussions… ?
marion.maisonobe@univ-tlse2.fr
sebastien.plutniak@ehess.fr

Plutniak maisonobe resto atelier2-network

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