cograph-package: cograph: Modern Network Visualization for R

In cograph: Analysis and Visualization of Complex Networks

cograph: Modern Network Visualization for R

Description

A modern, extensible network visualization package that provides high-quality static network plots and ggplot2 conversions. cograph accepts adjacency matrices, edge lists, or igraph objects and offers customizable layouts, node shapes, edge styles, and themes.

Main Functions

• cograph: Main entry point for creating network visualizations

• sn_layout: Apply layout algorithms

• sn_nodes: Customize node aesthetics

• sn_edges: Customize edge aesthetics

• sn_theme: Apply visual themes

• sn_render: Render to device

• sn_ggplot: Convert to ggplot2 object

Layouts

cograph provides several built-in layouts:

• circle: Nodes arranged in a circle

• spring: Fruchterman-Reingold force-directed layout

• groups: Group-based circular layout

• custom: User-provided coordinates

Themes

Built-in themes include:

• classic: Traditional network visualization style

• colorblind: Accessible color scheme

• gray: Grayscale theme

• dark: Dark background theme

• minimal: Clean, minimal style

• viridis: Viridis-based colour theme

• nature: Nature-inspired colour theme

Weight conventions

cograph's analytic functions follow a single convention for edge weights:

• Semantics. A weight is a strength: higher weight means a stronger connection (larger transition probability, thicker correlation, stronger tie). This matches the qgraph / tna convention and the intuition of most user-facing inputs.

• Path-based measures (betweenness, closeness, harmonic, eccentricity, stress, load, radiality, etc.) invert weights to distances via 1 / weight ^ alpha. The alpha argument (default 1) tunes how strongly weight differences compress paths. Controlled by the invert_weights argument, which auto-detects to TRUE for tna objects and FALSE for matrices/igraph (matching native igraph / sna defaults).

• Non-path measures (degree, strength, eigenvector, PageRank, transitivity, modularity, ...) use the raw weights as-is without inversion.

• Unweighted override. Passing weights = NA to any analytic function forces unweighted behavior regardless of what is attached to the graph.

Individual functions may document exceptions in their own help pages. Any deviation from this convention is a bug — please report.

Author(s)

Maintainer: Sonsoles López-Pernas [e-mail] [copyright holder]

Authors:

• Mohammed Saqr [copyright holder]

See Also

Useful links:

• https://sonsoles.me/cograph/

• https://github.com/sonsoleslp/cograph

• Report bugs at https://github.com/sonsoleslp/cograph/issues

cograph documentation built on May 31, 2026, 5:06 p.m.