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cograph

cograph is a modern R package for the analysis and visualization of complex networks, designed for simplicity, tidy outputs, comprehensive statistics and up-to-date network science. cograph accepts matrices, edge lists, and igraph, statnet, qgraph and tna objects without conversion, and offers a wide array of tools for plotting, wrangling, centrality, community detection, motif, robustness, multilayer and higher-order analysis.

Installation

# Release version from CRAN
install.packages("cograph")

# Development version from GitHub
# install.packages("remotes")
remotes::install_github("sonsoleslp/cograph")

Quick start

The examples use regulation_net, a synthetic weighted transition network among ten learning states included in the package. splot() plots it in one call, and tna_styling = TRUE applies the visual conventions of transition networks.

library(cograph)
splot(regulation_net, tna_styling = TRUE)

centrality() returns any combination of measures as a tidy data frame, from the classical measures to recent ones such as randomized shortest-path betweenness and Trust-PageRank.

centrality(regulation_net,
           measures = c("strength", "betweenness", "pagerank",
                        "rsp_betweenness", "trust_pagerank"),
           sort_by = "pagerank", digits = 3)
#>          node strength_all betweenness pagerank rsp_betweenness trust_pagerank
#> 1     Monitor         1.87        18.0    0.184         132.027          0.147
#> 2      Create         1.64        13.0    0.138         102.913          0.117
#> 3     Reflect         1.39        10.0    0.125          79.405          0.084
#> 4       Adapt         1.77        15.0    0.124          91.887          0.112
#> 5     Explore         1.39         5.0    0.118          79.715          0.086
#> 6       Share         1.95         9.0    0.095          69.907          0.092
#> 7    Evaluate         1.71         3.0    0.074          51.387          0.092
#> 8     Discuss         1.53         0.5    0.068          43.823          0.088
#> 9  Synthesize         0.77         6.5    0.038          23.304          0.067
#> 10       Plan         1.90        15.5    0.036          22.235          0.114

plot_mcml() shows a network whose nodes belong to clusters as a two-layer hierarchy, with the node-level network below and the cluster-level network above.

clusters <- list(Cognitive  = c("Explore", "Plan", "Monitor", "Adapt", "Reflect"),
                 Social     = c("Discuss", "Synthesize", "Share"),
                 Evaluative = c("Evaluate", "Create"))
plot_mcml(regulation_net, clusters)

plot_simplicial() visualizes higher-order pathways over the network, with each pathway joining the states that lead to a target state.

plot_simplicial(regulation_net,
                c("Explore Plan -> Monitor", "Monitor Adapt -> Reflect",
                  "Discuss Synthesize -> Evaluate", "Create Share -> Explore"))

What cograph covers

• Visualization. splot() plots any supported input with specialized styling for transition and psychological networks, alongside a wide array of specialized plots from alluvial flows and chord diagrams to bootstrap forest plots and temporal prisms.
• Wrangling. cograph offers a family of wrangling verbs for selecting, filtering, thresholding, transforming and editing networks, each returning a network so that the verbs chain with the native pipe.
• Centrality. centrality() returns a large collection of node centrality measures across all major families as a tidy data frame, tested against igraph, sna, centiserve, NetworkX and other implementations where they exist.
• Network statistics. network_summary() returns density, diameter, centralization, reciprocity, transitivity and many further statistics in one data frame.
• Communities. communities() runs a range of detection algorithms through one call, with consensus, comparison and significance testing of partitions.
• Motifs. motifs() and subgraphs() count the triads of the MAN classification, test their frequencies and identify the nodes that form each pattern.
• Robustness. robustness() and vulnerability() simulate targeted and random attacks and measure each node’s contribution to the efficiency of the network.
• Clusters, layers and higher-order structure. cograph offers hierarchical plots for multi-cluster networks, supra-adjacency tools for multilayer networks, and visualization of higher-order pathways estimated with Nestimate.

Documentation

Tutorials

• Network visualization with cograph: a complete guide to plotting with splot().
• Communities and higher-order networks: detecting communities and visualizing them over the network.
• Network estimation with Nestimate and cograph: from sequence data to bootstrapped, compared and clustered networks.
• Multi-cluster multi-level visualization: hierarchical plots of clustered networks with plot_mcml().
• Higher-order network analysis with simplicial complexes: from transition networks to topological analysis.

Articles

• Introduction to cograph: an overview of the package.
• Why cograph?: the design of the package.
• Centrality catalogue: every centrality measure with its definition and interpretation.
• cograph and the Centrality Zoo: cograph’s measures compared with the Zoo and with other packages.
• Plotting TNA models: a gallery of TNA plots.
• Advanced MCML examples: further multi-cluster figures.
• Bootstrap forest plots: confidence intervals of bootstrapped edges.
• Migrating from qgraph to splot: qgraph arguments and their cograph equivalents.

Citation and license

Please cite cograph with citation("cograph"). cograph is released under the MIT license.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.