[network-bipartite] Bipartite Network Graph

[MarkusNeusinger]

Description

A network visualization with two distinct sets of nodes arranged in separate columns or groups, where edges only connect nodes from different sets (never within the same set). This two-mode network layout is fundamental for visualizing relationships between two different types of entities.

Applications

• Author-paper affiliation networks in bibliometrics
• User-product purchase or recommendation networks
• Gene-disease association networks in bioinformatics
• Student-course enrollment visualization

Data

• source (str) — node from set A
• target (str) — node from set B
• weight (float, optional) — edge weight/strength
• Size: 10–50 nodes per set, 20–200 edges

Notes

• Two clearly separated node groups (left/right or top/bottom)
• Node size can encode degree (number of connections)
• Edge width or opacity can encode weight
• Color nodes by set membership
• Optional: highlight specific paths or clusters

[claude]

✅ Spec ID: network-bipartite

Rationale: Uses the established network plot type with bipartite variant, clearly describing the two-mode structure that distinguishes it from existing network specs (basic, directed, force-directed, hierarchical, weighted, transport-static).

This is a valid and useful plot type — bipartite layouts are a fundamental network visualization paradigm with broad applications across bibliometrics, recommendation systems, and bioinformatics. No existing spec covers the two-mode constraint where edges only connect nodes between distinct sets.

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: network-bipartite

PR: #5262

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specification.md

network-bipartite: Bipartite Network Graph

Description

A bipartite network graph visualizes relationships between two distinct sets of entities, where edges only connect nodes from different sets — never within the same set. The two node groups are arranged in separate columns or rows, making the two-mode structure immediately apparent. This layout is fundamental for understanding cross-category relationships, revealing which entities from one set are linked to which entities in the other, and exposing patterns like hubs, clusters, and isolated nodes.

Applications

• Author-paper affiliation networks in bibliometrics showing which researchers contributed to which publications
• User-product purchase or recommendation networks revealing consumption patterns and shared preferences
• Gene-disease association networks in bioinformatics mapping known links between genetic markers and conditions
• Student-course enrollment visualization displaying registration patterns across academic programs

Data

• source (str) — node identifier from set A (e.g., authors, users, genes)
• target (str) — node identifier from set B (e.g., papers, products, diseases)
• weight (float, optional) — edge weight representing connection strength or frequency
• Size: 10–50 nodes per set, 20–200 edges
• Example: A network of 15 researchers and 20 papers, with edges connecting each author to their publications

Notes

• Arrange the two node sets in clearly separated columns (left/right) or rows (top/bottom)
• Color nodes by set membership to reinforce the bipartite structure
• Node size should encode degree (number of connections) to highlight hubs
• Edge width or opacity can encode weight when weight data is provided
• Labels should be legible and positioned to avoid overlap with edges
• Keep the layout clean — use straight or slightly curved edges to reduce visual clutter

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Next: Add approved label to merge this specification to main.

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🤖 spec-create workflow

[github-actions]

🚀 Specification Merged!

Specification: network-bipartite
PR: #5262 (merged)

The specification is now in main and synced to PostgreSQL.

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Next: Add generate:{library} labels to start generating implementations.

Available libraries: matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, highcharts, letsplot

Or use workflow dispatch for bulk operations.

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🤖 spec-create workflow