| Maintainer: | Fabio Ashtar Telarico, Pavel N. Krivitsky, James Hollway |
| Contact: | Fabio-Ashtar.Telarico at fdv.uni-lj.si |
| Version: | 2025-12-05 |
| URL: | https://CRAN.R-project.org/view=NetworkAnalysis |
| Source: | https://github.com/cran-task-views/NetworkAnalysis/ |
| Contributions: | Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see theContributing guide. |
| Citation: | Fabio Ashtar Telarico, Pavel N. Krivitsky, James Hollway (2025). CRAN Task View: Network Analysis. Version 2025-12-05. URL https://CRAN.R-project.org/view=NetworkAnalysis. |
| Installation: | The packages from this task view can be installed automatically using thectv package. For example,ctv::install.views("NetworkAnalysis", coreOnly = TRUE) installs all the core packages orctv::update.views("NetworkAnalysis") installs all packages that are not yet installed and up-to-date. See theCRAN Task View Initiative for more details. |
This CRAN task view provides a curated list of R packages for analyzing and modelling networks (also known asrelational data orgraphs). These tools facilitate the exploration of natural, social, and other phenomena by focusing on the relationships between entities.
This page lists a number of packages, and sometimes core functions, in several sections based on their scope and focus:
The first section outlines the main ecosystems of R packages that include basic network-analytic operations such as creating, manipulating, and describing relational data. Here we also list choices of graphical packages for visualizing or drawing networks. For those new to network analysis in R, we recommend starting with theigraph introduction (Csárdi and Nepus 2006) or thestatnet tutorial (Bojanowski and Jasny 2024).
Subsequently, packages and functions for advanced network-analytical tasks are presented. We currently structure these into three subsections: (1) centrality, (2) community detection, and (3) model-based clustering.
Then, packages offering modelling and inferential tools applicable across disciplines and fields of interest are discussed. A distinction is drawn between models that are primarily for cross-sectional anddynamic data, with an extra section on special models for multimodal, multilevel, and multiplex data.
Finally, the focus shifts to packages containing data structures, methods, and models with a narrower field of application. The list includes some of the areas where network methods are more widely applied: ecology, bibliometrics, life and natural sciences, neurosciences, psychology, public health, social sciences and economics.
The list excludes packages that primarily deal with graph representations of conditional in/dependence between variables. This includes Bayesian networks and Markovian graphs, which, despite their relevance to statistical modeling, are covered under the CRAN task viewGraphicalModels. This distinction keeps the list focused on network analysis to explore broader relational dynamics.
Some packages could appear under multiple headings because they can perform multiple tasks (e.g., clustering and visualization). But, for the sake of brevity, non-core packages are listed only once: in the section that described each package’s main use case.
If you think that a package is missing from the list, please file an issue in the GitHub repository or contact the maintainer.
The starting point for analyzing networks in R is to familiarize with the main package ‘families’ or ecosystems. Using them, users can access functions to create, import/export, edit, and otherwise operate on relational data.
igraph provides tools for creating, manipulating, and analyzing network structures with a focus on graphical representations and fast algorithms to operate on large datasets (particularly manipulating data and dealing with vertex attributes). Rather than coming bundled with other packages,igraph offers an ecosystem of extensions and add-ons.
Approach: This R package is built upon a C library that is shared also by implementations in Python and Mathematica. The C code is efficient, and the R interface is increasingly consistent and easy to use with a lot of basic functionality, including calculating network properties, generating random graphs for simulations, etc.
Flexibility: Many of the functions are provided in two versions: for direct assignment and a pipe-able version.
Support: There are tons of online resources answering virtually any question concerninghow to do almost anything thanks to a large community and active maintainers.
Extensions: There are a number of add-on packages that integrate igraph withggplot2 and other drawing tools or provide sample datasets. According to the latest review this is the largest network-analysis ecosystem in R by number of extensions (see Kanevsky 2016).
Statnet is a suite of R packages for analysis and statistical modeling of networks that forms a core of an ecosystem of packages for statistical network analysis built upon common data representations (particularly thenetwork package) and design choices. Meta-packager pkg("statnet", priority = "core") makes it easy to install the core packages in the suite.
Approach: The approach in this group of packages is more disaggregated and involves a number of different packages for different purposes. In particular, whereasigraph implements both the data structure and network analysis methods,network does not implement network analysis methods but relies on other packages, such assna and theergm family.
Flexibility: The packages in the Statnet suite can be used to analyze (social) network data using both direct assignment and a ‘piped’ approach.
Comprehensiveness: This ecosystem’s biggest advantage is that it allows users to carry out most network-analytical operations, especially in dealing with social network analysis (SNA). But the trade-off, of course, is that the number of possibilities makes life harder for new users.
Stocnet is an open software system for the advanced statistical analysis of social networks. Its history reaches back to 1998, but was reestablished in 2024 to support the continued development to consistent standards of a set of packages that span the network analysis pipeline, from creating and modifying many different types of networks to their analysis and inference:
Although the ‘core’ packages for network analysis in R can create a wide range of networks from different types of inputs, there are also specialized packages for constructing more specialized formats or for converting or coercing between different formats.
network andigraph provide basic data structures and tools for creating, importing, modifying, and exporting their respective representations of relational data.
manynet is built upon other network packages in this list and offers interoperability with many different classes of objects as well as network analytic tools. Its coercion routines retain more information than the long-standing alternativeintergraph, being able to transform between more classes and receiving more frequent updates. It also includes more complete import and export routines than generalist packages. It offers a piped and explicit syntax, which helps new users and those working with multiple packages alike. The package also offers straightforward tutorials and examples to help users get started with network analysis in R. Further explanation, examples, and references are continually being to the documentation to provide user reference and support user experience.
intergraph is not a network analysis packageper se. Rather it allows to easily convert objects produced by Statnet packages intoigraph objects (or a data frame) and vice versa. Thus, it helps leveraging multiple packages’ functionalities and ensuring compatibility between several users’ workflows too many additional functionalities.
Similarly,netUtils supplies a collection of helper functions for working with network objects including the extraction of sub‑graphs, computing basic statistics, converting between common network classes (not least from/to igraph and the statnet suite).
BoolNet provides tools for assembling, analyzing and visualizing synchronous and asynchronous (probabilistic) Boolean networks as well as simpler Boolean networks. All the main functions are described in a handyvignette.
egor provides tools for managing ego-centric networks, including importing from exports fromEgoNet,EgoWeb 2.0 andopeneddi. It includes a Shiny app and procedures for creating and visualizing clustered graphs.
networkDynamic from Statnet facilitates representation and manipulation of dynamic networks.
ionet creates network starting by turning input-output tables into weighted adjacency matrices.
rgraph6 allows to encode relational data (adjacency matrices, edge lists,network andigraph objects) as ASCII strings and vice versa usinggraph6,sparse6, anddigraph6formats.
tidygraph is designed for handling and manipulating graph data within thetidyverse framework. It does not make it into “core” packages because it lacks a comprehensive set of tools for network analysis. Yet, it provides a flexible piped approach to working with relational data, allowing users to apply familiar data manipulation techniques from the tidyverse to graphs. Users can easily perform tasks such as filtering, summarizing, and joining graph data using familiar tidyverse syntax. Given that bothigraph andsna provide piped functions for most operations,tidygraph’s added value lies mainly in the possibility of accessing directly either the node data, the edge data or the graph itself while computing inside verbs.
backbone implements extraction of a sparse and unweighted subgraph of a network called abackbone.
Moving to Exploratory Data Analysis (EDA),igraph,sna, andmanynet offer functions for a similar set of network-analytic and visualization operations, whereastidygraph is more limited. However, some algorithms differ from each other and from those are some specialized packages for their implementation, speed, or defaults.
manynet offers functions for returningmarks (logical scalars or vectors),measures (numeric scalars or vectors), andmemberships (string scalars or vectors) at the network, node, or tie level. For example,manynet::net_* functions always return a single value (scalar), andmanynet::node_* andmanynet::tie_* always return a vector the length of the nodes or ties in the network, respectively:
manynet leverages S3 dispatching so that all the functions work with manyclasses of network objects, such as network orgraph objects, but also edge lists, matrices, and various other more specific classes.
manynet wraps many igraph functions but extends or corrects them to treat manytypes of networks, including two-mode, multiplex, signed, and dynamic networks. The package also implements various functions for network analysis unavailable elsewhere.
tsna implements a number of methods for exploratory analysis and summaries of temporal networks in thenetworkDynamic representation.
Reletadly to EDA,NetworkDistance offers many measures to compute the distance between two networks based on centrality, continuous spectral densities, the Euclidean distance between the adjacency matrices’ spectra, the Frobenius norm of edge-to-edge difference, exponential kernel matrices, graphons, the discrepancy between two binary networks for each edge (Hamming), a combines the local Hamming distance and the global Ipsen-Mikhailov distance, and the log of graph moments.
netseg implements a collection of descriptive measures of network segregation and homophily such as Freeman Index, Coleman Index, Spectral Segregation Index and more.
diagram was born as a companion to the bookA Practical Guide to Ecological Modelling by K. Soetaert and P.M.J. Herman. But it can visualize as a flow diagram, a web or grid any network given in the form of a transition matrix.
neatmaps tries to simplify the exploratory step of data analysis by providing function to easily produce hierarchical clustering (neatmaps::hierarchy), consensus clustering (neatmaps::consClustResTable) and heatmaps of multiple networks (neatmaps::neatmap).
autograph builds onggraph for drawing graphs and plots of network objects with sensible defaults and consistent theming in a range of institutional styles.
autograph::graphr is for quick, easy network visualization.
autograph::graphs is for comparing ego networks or subgraphs side by side.
autograph::grapht is for developing dynamic or longitudinal networks into GIFs.
The package includes plotting methods for output from a range of Stocnet packages, including blockmodels and dendrograms for clustering, but also diagnostics and goodness-of-fit forRSiena,MoNAn, andmigraph.
multigraph is a powerful tool providing easier visualizations of multigraphs, various types of networks (multilevel/multiples, temporal, spatial, bipartite, valued, signed), and Cayley graphs with various layout options.
ggraph allows to plotigraph objects by building up plots layer by layer.
ggsom offers functions to plot self-organizing maps (SOMs).
roughnet leverages therough.js library to draw sketchy, hand-drawn-like networks
graphlayouts adds several layout algorithms toigraph andggraph based on the concept of stress majorization (See alsoedgebundle).
autograph includes a few more layout algorithms for multimodal networks as well as sensible defaults for the automatic plotting of many graph objects.
Rgraphviz, available on Bioconductor, creates a direct link between thegraph package and thegraphviz library.
ggdendro makes it easy to make ggplots of dendrograms create using the functionstree,hclust,dendrogram, andrpart.
More details in the CRAN task viewDynamicVisualizations.
visNetwork focuses on interactive network visualization using thevis.js library. The package allows users to create visually appealing and interactive network visualizations with features such as zooming, panning, and node highlighting. It offers a user-friendly interface for creating interactive network visualizations, making it suitable for un-experienced users.
networkD3 provides functions that turns edge lists into aD3 JavaScript network, tree, dendrogram, or Sankey plots.
snahelper is an add-on allowing access to a GUI for visualizing and analyzing networks. Once the visualization is set, the relevant code is automatically added to the script.
bipartiteD3 uses theD3 andviz.js libraries for plotting networks produced with thebipartite package.
ndtv renders network objects from the packagenetworkDynamic as videos or interactive animations.
g6R provides an wrapper for theG6.js graph visualisation engine, letting the user build interactive network visualisations with dozens of layouts, animations, and user‑interaction behaviours. In includes several plugins and the graphs are easily embedded in Shiny apps.
Both main ecosystems can compute betweenness, eigenvalue, power, and closeness centrality, butigraph offers more options thansna andtidygraph overall. In addition:
centiserve adds dozens of centrality measures for igraph objects such as bottleneck, decay, and entropy centrality.
birankr provides optimized functions for estimating various centrality measures in bipartite/two-mode networks. It can also estimate efficiently page-rank in one-mode networks, project two-mode networks to one-mode ones, and convert edge lists and matrices to thesparseMatrix format offered in the packageMatrix. It supports edge lists (in thedata.frame,data.table::data.table, ortidydata::tbl_df class) and adjacency matrices (either in the built-inmatrix class or inMatrix’sdgCMatrix class).
netrankr offers index-free centrality rankings vianeighborhood-inclusion orpositional dominance and based on probabilistic methods like computing expected node ranks and relative rank probabilities.
influential provides a collection of tools designed to help users work with networks and understand their structure and properties including analyzing network topology and calculating several centrality measures. In addition, it provides unsupervised centrality ranking based on influence through a Susceptible–Infected–Recovered model with leave-one-out cross validation (a machine learning technique). Another interesting advanced function is the ability to compute dependence and correlation between pairs of centrality measures.
CINNA is a toolkit designed to help researchers analyze networks and identify the most “central” nodes. Notably, CINNA supports bipartite networks, where nodes are divided into two groups. The package includes some centrality measures not available in other R packages such as Dangalchev centrality (closeness centrality for disconnected networks), group centrality; local bridging centrality; harmonic centrality; wiener index centrality (i.e., the network’s overall efficiency based on distances). It also allows to use t-SNE (t-distributed stochastic neighbor embedding) or PCA (Principal Component Analysis) to help determine which centrality measure is most informative for a given network. Moreover, CINNA provides various ways to visualize centrality: heatmaps (compare nodes across centrality measures), dendrograms (grouping similarly central units), scatterplots (between pairs of centrality measures).
igraph is the package of choice for the implementation of most modularity-based community-detection algorithms. Available approaches include betweenness, greedy algorithm, infomap, label propagation Leiden, Generalized Louvain, and walktrap amongst others.
cencrne proposes a regularized network-embedding model to simultaneously estimate the community structure and the number of communities in an asymptotically consistent way. The method is mainly used in life sciences but is applicable across the board.
sna implements a simple version of structural-equivalence blockmodel (sna::blockmodel). It can also generate networks with a given blockmodel as well as print and plot the results.
concorR implements the classical CONCOR (Convergence of iterated Correlation) algorithm for one- and multi-mode un/directed networks.
BMconcor allows the simultaneous blockmodeling of networks based on structural and regular equivalence through singular value decomposition (SVD) by blocks.
blockmodeling: this package offers and implementation of generalized blockmodeling (blockmodeling::optRandomParC) as well as functions for computation of (dis)similarities in terms of structural or regular equivalence and plotting. Furthermore, it includes implementations of the REGE algorithm (blockmodeling::REGE).
BlockmodelingGUI is a Shiny app providing a graphical interface for generalized blockmodeling of single-relation, one-mode networks from the packageblockmodeling. It includes several ways to visualize networks and partitions usingigraph,network, and more.
kmBlock implements a k-means like approach to the blockmodeling of one-mode and linked networks.
dBlockmodeling contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links), one-mode and valued one-mode and two-mode.
signnet offers to functions implementing the generalized blockmodeling with structural equivalence (signnet::signed_blockmodel) and generalized equivalence (signnet::signed_blockmodel_general) of signed networks based on objects fromigraph
oaqc enables efficient computation of the orbit-aware quad census.
igraph cannot run SBMs, but it can generate a random graph according to a specified SBM (igraph::sample_sbm) or an arbitrary hierarchical SBM (igraph::sample_hierarchical_sbm)
blockmodels allows to run the SBM or the Latent Block Model (LBM, an SBM for bipartite networks) of static networks using a Variational Expectation Maximization algorithm. VariousS4 functions implement three probability distributions:blockmodels::BM_bernoulli for binary data,blockmodels::BM_poisson for discrete/count weights,blockmodels::BM_gaussian for continuous weights. It allows for SBMs and LBM with or without node covariates and supports multiplex binary networks viablockmodels::BM_bernoulli_multiplex.
sbm is an extension ofblockmodels for bi- and multi-partite as well as multiplex networks through dedicatedR6 classes. It includes functions to plot the resulting partition.
greed leverages a combination of greedy local search and a genetic algorithm to execute (degree-corrected) SBM and LBM.
dynsbm, implements the model for temporal networks which combines a static SBM with independent Markov chains for the dynamic part. It supports binary and weighted networks with both discrete and continuous edges. Includes also functions for plotting (dynsbm::adjacency.plot,dynsbm::alluvial.plot,dynsbm::connectivity.plot) the partition and automatically constructs matrices as an array of the right format.
MLVSBM(archived) Implements the SBM of multilevel networks where the different matrices each represent an interaction layer either weighter or binary. It generalizes the original approach to more than two layers.
StochBlock implements the stochastic blockmodeling of one-mode and linked networks. It includes utilities to plot the results but cannot choose automatically the ‘right’ number of clusters and tends to be very slow according tosubsequent reviews.
GREMLINS implements the SBM of generalized multipartite networks where the different matrices each involve nodes that can be partitioned into a-priori definedfunctional groups.
clustNet allows to cluster units in a network using a Bayesian mixture model that can account for node and edge covariates.
collpcm provides Monte-Carlo Markov Chain (MCMC) inference for collapsed latent space models that allow to search over the model space, including deciding on the number of clusters.
graphclust implements an agglomerative algorithm to maximize the integrated classification likelihood criterion and a mixture of stochastic block models based onigraph objects.
latentnet provides functions to fit and simulate latent position and cluster model usingnetwork objects and compatibly withergm approaches.
relatedly,VBLPCM offers an alternative tolatentnet for larger networks (on which the latter’s package algorithm may be computationally prohibitive). It computes the approximation of the posterior of thelatentnet::ergmm() function using a Variational Bayesian Expectation Maximisation algorithm. Thus, it is faster than the full-fledged MCMC sampler more accurate thanlatentnet’s two-stage maximum likelihood estimation (MLE). Indeed, Variational Bayes tends to converge quicker than the two-stage MLE, too.
latenetwork implements a method for causal inference with noncompliance and network interference of unknown form on average causal using instrumental variables.
netClust provides a function to cluster one-layer (netClust::netEM_unilayer) and multilayer (netClust::netEM_multilayer) networks by means of finite mixtures and expectation-maximization.
Statistical modelling in network analysis enables researchers to uncover patterns, test hypotheses, and make predictions about network structures and dynamics. This section introduces R packages that support a range of statistical approaches, from modelling static (cross-sectional) networks to analyzing dynamic, multimodal, and multilevel networks. These methods provide tools to infer underlying processes that generate observed network data, assess the significance of observed patterns, and simulate network structures under various conditions.
ergm from thestatnet ecosystem provides functions to fit, simulate and analyze exponential-family random graph models (ERGM). Depending on specific needs, several specialized extensions are available.
| Use case | Package |
|---|---|
| Count weights | ergm.count |
| Egocentrically sampled networks | ergm.ego |
| Multilayer networks and samples of networks | ergm.multi |
| Networks with block structure and local dependence | mlergm |
| Rank-order networks | ergm.rank |
| Modeling ERGM-generating processes | ergmgp |
| Samples of small networks | ergmito(archived) |
| Large hierarchical ERGMs | bigergm |
| Bayesian methods for ERGMs | Bergm |
| Template for implementing custom network effects (non-CRAN) | ergm.userterms |
| User-contributed network effects (non-CRAN) | ergm.terms.contrib |
amen offers additive and multiplicative effect (AME) models with regression terms, covariance structure of the social relations model, and multiplicative factor models. It supports binary networks as well as valued ones (assuming a Gaussian, zero-inflated/tobit, ordinal, or fixed-rank nomination model)
bootnet implements bootstrap procedures to assess accuracy and stability of estimated structures and centrality indices on undirected networks (For an alternative seelocalboot).
fastnet allows to simulate large-scale social networks and retrieve their most relevant metrics following a new approach.
nda gathers non-parametric dimensionality-reduction functions with/out (automated) feature selection and limited plotting capabilities.
lolog implements Latent Order Logistic (LOLOG) models, a network formation process in which edges are added one at a time drawn from a distribution conditional on edges already added, with order unknown.
MoNAn implements the method to analyze the structure of weighted mobility networks or distribution networks outlined.
ERPM implements an exponential-family model for cross-sectional or longitudinal partitions, i.e. non-overlapping sets of groups, such as sports teams, animal herds, or political coalitions, through group formation processes based on individual attributes, relations between individuals, and size-related factors.
networkscaleup implements methods for estimating, among other things, degree and sizes of hidden populations based on Aggregated Relational Data (ARD) – number of people known in a set of sub-populations. The methods include “classical” MLE estimators as well as more recent Bayesian models.
migraph is anigraph extension to analyze multimodal networks.
multinets is anigraph extension to analyze multilevel networks.
multiplex makes possible, among other things, to create and manipulate multiplex, multimode, and multilevel network data with different formats.
dyads offers functions for the MCMC simulation of dyadic network models j2, p2 (also multilevel) and b2 model.
tnet includes functions for analyzing two-mode, weighted, and longitudinal networks.
incidentally implements methods to generate two-mode networks consistent with a given one-mode network.
ergm.multi is a set of extensions toergm for modeling multilayer and multimode networks, as well as samples of networks.
The following packages focus on modeling and simulation of networks that evolve over time and network processes that occur over time.
Relational event models (REMs) are used to describe data containing information about the exact times during which the nodes interact. This is commonly observed for e-mail, radio, and other communications.
rem andrelevent both contain functions to fit and simulate dyad-oriented relational event models. But onlyrelevent can estimate event sequence data without time stamps.
goldfish offers functions to fit and simulate actor-oriented dynamic network actor models and dyad-oriented relational event models.
dream provides scalable tools for analysing large REMs. It enables efficient risk-set formation (incl. via case-control sampling) using the functiondream::processTMEventSeq as well as the computation of a wide range of REM-specific descriptive statistics (e.g. dream::computeReciprocity,dream::computeTriads, etc.). In addition, it allows the estimation or simulation of relational event sequences and implements recently proposed brokerage and structural hole measures in one- and two-mode networks.
The following package are focused on modeling series of networks, also known as panel data.
tergm a set of extensions forergm for fitting and simulating discrete-time models for series of networks (or a long-term equilibrium of a discrete-time network process) where each time step is modeled as a draw from an ERGM conditional on the prior time steps.
dnr estimation of discrete-time models for series of networks where each time-step is modeled as a draw from an ERGM conditional on prior time steps, subject to the constraint that within each time step, edge variables are independent. Varying node sets are also supported.
btergm bootstrap inference for discrete-time models for series of networks where each time step is modeled as a draw from an ERGM conditional on the prior time steps.
RSiena estimation of continuous-time Stochastic Actor-Oriented Models (SAOMs) for panel network data.
idopNetwork implments the model to convert static data into their ‘dynamic’ form contextually inferring informative, dynamic, multi-directional networks with clusterable structures.
EpiModel allows to simulate mathematical models of infectious disease dynamics.
manynet can manipulate, visualize, and analyze longitudinal and network event data, including running contagion/diffusion processes and compartmental models.
netdiffuseR was developed for empirical statistical analysis, visualization and simulation of network diffusion and contagion processes. It implements algorithms for calculating network diffusion statistics such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility.
As an interdisciplinary approach, network analysis is used in a number of fields, where the specific needs and interests of those fields are addressed by particular packages.
econetwork is a collection of advanced functions to analyze and models of ecological networks (mainly food webs and host-parasite relations, but also plant-pollinator and other mutualistic ones) statically and dynamically.
AnimalHabitatNetwork(archived) provides functions for generating and visualizing networks representing the physical configurations of animal habitats. It implements an original network-generating algorithm based on pair-wise Euclidean distances and can output undirected network either weighted or binary, fully connected or sparse). The package is associated with a PDF on modelling the physical configurations of animal habitats using networks.
aniSNA allows to obtain network structures from animal GPS telemetry observations and statistically analyze them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics.
asnipe implements several tools that are used in animal social network analysis to cluster, and generate networks, perform permutation tests, calculate association rates, and perform multiple regression analysis.
ATNr estimatesallometric trophic models (ATN) for the species biomasses in dynamic food-webs and allows to generate synthetic networks. It also provides access to the ODE solver deSolve.
BIEN allows to access theBotanical Information and Ecology Network Database in R
bipartite offers functions to visualize food webs and calculate some ecological indices on them.
cassandRa deals with under-sampling in ecological networks by fitting a variety of statistical models and sample coverage estimators to correct for (likely) missing ties. It works only on bipartite networks.
EcoNetGen to simulate and sample from ecological networks.
econullnetr to carry out null-model analysis for ecological networks.
bibliometrix includes functions to import bibliographic data from the main publication databases online (‘SCOPUS’, ‘Clarivate Analytics Web of Science’, ‘Digital Science Dimensions’, ‘Cochrane Library’, ‘Lens’, and ‘PubMed’). It can also build networks (bibliometrix::biblioNetwork) for co-citation, coupling, scientific collaboration and co-word analysis including their dynamic versions (bibliometrix::histNetwork). It allows to plot the data usingVOSviewer.jar.
bibliometrixData contains example datasets for testingbibliometrix.
biblionetwork proposes functions to identify and weight the edges in a bibliometric network. All functions are optimized for large datasets. It implements different methods for different types of relations: Co-authorship supports simple counting, (refined) fractional weight with or with cosine normalization. Bibliographic coupling supports: coupling strength and angle. Co-citation supports the cosine normalization of count weights.
Diderot is geared towards the analysis of citation networks using modularity and heterocitation metrics based on Scopus data.
Rcy3 provides access toCytoscape, one of the most used network tool in the field of molecular biology, allowing to vizualize, analyze and explore networks using a single function for each operation executable through Cytoscape’s graphical interface.
WGCNA focuses on the analysis of weighted correlation networks. It has functions for network construction, modularity computation, gene selection, topological analysis, generating data, plotting, and exports to third-party software. Notably, the underlying data mining approach has been used beyond the natural sciences. There are several packages on Bioconductor that reverse-depend/extend these functionalities.
c3net allows to infer gene-regulation networks with direct physical interactions usingC3NET. Other packages implement improvements/variants of this algorithm based on the literature, such as:
Ac3net Infers directional conservative causal core in gene network based on a new algorithm for directional network proposed.
bc3net implements the BC3NET algorithm for inference on gene-regulation networks. In essence it offers a Bayesian approach with noninformative prior to the C3NET algorithm.
BioNAR implements a detailed topologically based network analysis with functions that create networks based on laboratory-produced meta-data. It includes functions for vertex centrality measure and modularity computation. Additionally, it provides a robust synaptic proteome network for data validation.
BASiNET andBASiNETEntropy provide functions for classifying RNA sequences using network algorithms and notions from information theory.
bionetdata is a collection of relation datasets of biological and chemical nature.
Cascade includes functions for gene selection, reverse engineering, and prediction in cascade networks.
NetworkToolbox implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold and dependency. It can also execute some basic operations such as computing centrality of nodes and community or the network’s clustering coefficient.
qgraph provides tools for visualizing and analyzing weighted networks and a Gaussian graphical model for plotting. It is compatible withigraph through theqgraph::as.igraph.qgraph function. It is mostly used in psychology and neurosciences.
HospitalNetwork provides functions to construct a one-mode network of hospitals based on the linked two-mode networks of hospitalized patients’ transfers.
geonetwork handles networks or graphs whose nodes are locations. The functions includes the creation of objects of classgeonetwork as a graph with node coordinates, the computation of network measures, the support of spatial operations (projection to different coordinate reference systems, handling of bounding boxes, etc.) and the plotting of thegeonetwork object combined with supplementary cartography for spatial representation. It is compatible withigraph.
sfnetworks combines the work-horse spatial-data package in R (sf) andtidygraph for tidyverse-friendly classes and routines (shortest paths, cleaning, and editing) for geospatial networks.
spNetwork offers functions for analysing undirected and directed geographic networks. Implemented methods include network kernel density estimates, spatial weight matrices, network k function and k-nearest neighbours, and is compatible withigraph.
chessboard provides functions to work with un/directed undirected spatial networks. It allows to create connectivity matrices (chessboard::connectivity_matrix) and exports results to several formats: node list, neighbor list, edge list, connectivity matrix, Eigenvector maps. It also implements connectivity for chess pieces via specific functions:chessboard::bishop,chessboard::knight,chessboard::pawn,chessboard::queen,chessboard::rook, besides introducing two sets of movement ruleschessboard::fool andchessboard::wizard.
epanet2toolkit interfaces R with theEPANET programmer’s toolkit to carry out basic (epanet2toolkit::ENepanet) or customized (epanet2toolkit::ENopen) simulations.
intensitynet includes functions to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for un/directed and mixed networks
epinet simulates contact networks to predict the transmission of contagious diseases through Bayesian inference.
hybridModels offers a meta-population model that assigns nodes to sub-populations to better model disease spreading through cluster contagion using stochastic simulation algorithm and an individual-based approach.
netdiffuseR provides functions for calculating network effects such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility.
EpiModel builds on Statnet’s for epidemic modelling. But more on this field of application can be found in the CRAN task viewEpidemiology.
sna implements many operations commonly carried out on networks in the social and economic sciences with the ability of regress a network variable on others using ordinary least square, linear network autocorrelation models or a logistic regression (More on this type of applications can be found in the CRAN task viewGraphicalModels.
FinNet provides classes, methods, and functions to deal with financial networks involving both physical and legal persons. The package assists in creating various types of financial networks: ownership, board interlocks, or both. It support different tie-weighting procedures (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list,igraph,statnet).
ITNr gathers functions to clean and process international trade data into an adjacency matrix. It can also extract the network’s backbone, compute centrality, run blockmodels and other clustering procedures, or highlight regional trade patterns.
modnets models moderator variables in cross-sectional, temporal, and multi-level networks.
multinet provides functions for the creation/generation and analysis of multi-layer social networks
Bojanowski, Michal and Lorien Jasny.statnet tutorial.Introduction to Network Analysis Tools in R.https://statnet.org/workshop-intro-sna-tools/
Csárdi, Gábor, and Tamás Nepus. 2006.igraph introduction.igraph Reference Manual.https://igraph.org/c/doc/igraph-Introduction.html
Kanevsky, Gregory. 2016. “R Graph Objects: igraph vs. network.R Bloggers. January 30, 2016.https://www.R-bloggers.com/2016/01/r-graph-objects-igraph-vs-network/`