Gephi vs nodegoat

Verdict

Both tools deal in relationships between people, places and events, but they start from different points in a research project. Gephi assumes you already have (or can export) a network — nodes and edges in formats like GEXF, GraphML or CSV — and its job is to lay it out, compute network statistics like centrality and modularity, and produce a visualization, including a dynamic timeline view for networks that change over time. nodegoat assumes the opposite: that your historical or humanities data does not yet fit a fixed schema, and its job is to let you define your own data model of objects, relations, time and place, with network graphs, timelines and maps built in as views on top of that structured data once it exists.

Choose Gephi if

• You already have relationship data in a standard graph format, or can export it from a spreadsheet or database.
• You want free, open-source software you run locally, with a large plugin ecosystem and no ongoing subscription.
• Your priority is layout quality, network statistics and publication-ready visual export.

Choose nodegoat if

• Your data does not fit a network file format yet — you need to define objects, relationships, time and place from scratch as part of the research process.
• Your project is a multi-user team effort that benefits from a shared, cloud-based research environment rather than local files.
• You want built-in timeline and geographic mapping views alongside network graphs, not network visualization alone.

The honest caveat

nodegoat does not publish pricing — it is sold as institutional or project-based subscription licensing, so budget time to request a quote and understand training and workshop costs before committing, especially for a project without a big grant behind it. Gephi is free either way, but "free" still means someone needs to be comfortable installing, updating and troubleshooting a Java desktop application over the life of a multi-year project. If you are unsure which stage you are at, start by trying to sketch your data as a simple GEXF or CSV file — if that is already possible, Gephi alone may be enough.