Garphield graph concepts

Garphield keeps graph data separate from the sources used to analyse, filter, and present it. Sources can be combined without replacing the underlying data.

Graph data

Graph data contains the network: nodes, links, direction, keys, weights, and typed attributes. Garphield accepts graph files, tables, URLs, generated networks, and projects from Python or R. Loading new data replaces the open network.

A network can be directed or undirected, weighted, a multigraph, or contain self-loops.

See Load data and Supported file formats.

The source catalogue

The source catalogue is everything Garphield can use to describe or change a network view. It combines:

• underlying node and link data;
• network science algorithms such as Degree, Betweenness, and Louvain;
• application context such as the current selection and saved sets;
• transformations produced earlier in the pipeline.

Each source has a target—node, link, or graph—and a result type such as a number, category, or set. That lets Garphield show which visual channels, filters, and transformations can use it.

See Sources and algorithms.

Compose sources

Bind a source to size, colour, labels, or another visual channel. Use the same source in a filter, or feed a transformation into another algorithm. For example, Louvain communities can colour nodes, define a filter, and become saved sets without rerunning a separate workflow for each use.

The pipeline stays reversible. Change an input, remove a step, or return to an earlier entry in History.

See Explore and analyze and Visual channels.

Projects, URLs, and exports

A .gph project stores the network and workspace: positions, bindings, filters, history, sets, annotations, and story steps. A Garphield URL stores a shareable view for supported browser data. Data exports contain the network; PNG export contains the presentation.

See Save, share, and export, Project model, or The .gph document.