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[network-weighted] Weighted Network Graph with Edge Thickness #3290
Rationale: The focus on edge thickness as the primary visual encoding for connection weight/strength distinguishes this from existing network specs where weight encoding is optional.
network-weighted: Weighted Network Graph with Edge Thickness
Description
A weighted network graph displays relationships between entities using edge thickness to represent connection strength or weight. Unlike basic network graphs where edges are uniform, the varying line widths immediately communicate the relative importance of each relationship. This visualization makes it easy to identify strong vs weak connections, central hubs with many heavy links, and structural patterns in weighted relational data.
Applications
Analyzing trade flows between countries where thicker edges represent higher export/import volumes
Visualizing communication patterns in organizations where edge weight shows message frequency between teams
Mapping collaboration networks in research where thickness indicates number of co-authored papers
Displaying transportation networks where edge weight represents passenger volume or freight capacity
Data
nodes (list of dicts) - entities with unique IDs and optional labels or group attributes
edges (list of dicts/tuples) - connections as (source_id, target_id, weight) where weight is a positive numeric value
weight (numeric) - connection strength/intensity mapped to edge thickness
Size: 10-50 nodes for clear visualization of weighted edges without clutter
Example: Trade network with 15 countries where edge weights represent billions of USD in annual trade volume
Notes
Edge thickness should scale proportionally but remain visually distinguishable (avoid too thin or too thick extremes)
Consider a legend or annotation explaining the weight scale
Nodes can optionally be sized by weighted degree (sum of connected edge weights)
Use force-directed or spring layout to position nodes, allowing edge weights to influence attraction
Avoid edge overlap where possible to keep individual weights readable
Next: Add approved label to merge this specification to main.
PR:#12391 Generation: adopted from 5b2bcb3e5e4dc5b3685a669c07cf3ab1b9918f69 by gemini-4-argon Repair model: opus (forced regeneration) Interactive:light · dark
Activity
claude commentedon Jan 8, 2026
✅ Spec ID:
network-weightedRationale: The focus on edge thickness as the primary visual encoding for connection weight/strength distinguishes this from existing network specs where weight encoding is optional.
Add label
approvedto begin generation.spec: add network-weighted specification
[-]Weighted Network Graph with Edge Thickness[/-][+][network-weighted] Weighted Network Graph with Edge Thickness[/+]github-actions commentedon Jan 8, 2026
✅ Specification Ready:
network-weightedPR: #3292
specification.md
network-weighted: Weighted Network Graph with Edge Thickness
Description
A weighted network graph displays relationships between entities using edge thickness to represent connection strength or weight. Unlike basic network graphs where edges are uniform, the varying line widths immediately communicate the relative importance of each relationship. This visualization makes it easy to identify strong vs weak connections, central hubs with many heavy links, and structural patterns in weighted relational data.
Applications
Data
nodes(list of dicts) - entities with unique IDs and optional labels or group attributesedges(list of dicts/tuples) - connections as (source_id, target_id, weight) where weight is a positive numeric valueweight(numeric) - connection strength/intensity mapped to edge thicknessNotes
Next: Add
approvedlabel to merge this specification to main.🤖 spec-create workflow
spec: add network-weighted specification (#3292)
github-actions commentedon Jan 8, 2026
🚀 Specification Merged!
Specification:
network-weightedPR: #3292 (merged)
The specification is now in
mainand synced to PostgreSQL.Next: Add
generate:{library}labels to start generating implementations.Available libraries:
matplotlib,seaborn,plotly,bokeh,altair,plotnine,pygal,highcharts,letsplotOr use workflow dispatch for bulk operations.
🤖 spec-create workflow
149 remaining items
github-actions commentedon Sep 2, 2026
✅ muix Complete
muix implementation for
network-weightedhas been merged to main.PR: #10994
Quality Score: 90/100
Preview: light · dark
🤖 impl-merge
feat(highcharts): implement network-weighted (#10993)
github-actions commentedon Sep 2, 2026
✅ highcharts Complete
highcharts implementation for
network-weightedhas been merged to main.PR: #10993
Quality Score: 89/100
Preview: light · dark
🤖 impl-merge
feat(echarts): implement network-weighted (#10995)
github-actions commentedon Sep 2, 2026
✅ echarts Complete
echarts implementation for
network-weightedhas been merged to main.PR: #10995
Quality Score: 93/100
Preview: light · dark
🤖 impl-merge
github-actions commentedon Sep 2, 2026
🎉 All Implementations Complete!
All 15 library implementations for
network-weightedhave been successfully merged.🤖 impl-merge
github-actions commentedon Oct 24, 2026
🎨 javascript/muix Preview
PR: #12391
Generation: adopted from
5b2bcb3e5e4dc5b3685a669c07cf3ab1b9918f69by gemini-4-argonRepair model: opus (forced regeneration)
Interactive: light · dark
🤖 impl-generate
feat(muix): implement network-weighted (#12391)
github-actions commentedon Oct 24, 2026
✅ muix Complete
muix implementation for
network-weightedhas been merged to main.PR: #12391
Quality Score: 86/100
Regen: previous 90 → 86 (stored scores)
Preview: light · dark
🤖 impl-merge
github-actions commentedon Oct 24, 2026
🎉 All Implementations Complete!
All 15 library implementations for
network-weightedhave been successfully merged.🤖 impl-merge