feat(plotly): implement network-force-directed

[github-actions]

Implementation: network-force-directed - python/plotly

Implements the python/plotly version of network-force-directed.

File: plots/network-force-directed/implementations/python/plotly.py

Parent Issue: #990

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot renders on a warm off-white background (~#FAF8F1). Three distinct community clusters are clearly visible: Engineering (teal/green, ~#009E73), Marketing (orange, ~#D55E00), and Sales (blue, ~#0072B2) — appearing to follow Okabe-Ito order. Node sizes visibly scale by degree, with hub nodes notably larger. Semi-transparent gray edges connect nodes within and between communities. "Hub" text annotations appear above high-degree nodes in both clusters. The title "network-force-directed · plotly · anyplot.ai" is centered at top in dark text. A "Teams" legend sits at top-left with three color entries. All text is readable against the light background. Legibility verdict: PASS.

Dark render (plot-dark.png): The same layout renders on a near-black background (~#1A1A17). Data colors (teal, orange, blue) are visually identical to the light render — only the background flips. The title is rendered in light text and is clearly readable. The legend background adapts to dark. Hub annotation labels remain visible in both renders. No catastrophic dark-on-dark failure visible in images, though the code hardcodes #333333 for hub annotation text color which would be dark-on-dark — this is a lurking issue. Legibility verdict: PASS (with caveat on hub annotation color in code).

Discrepancy note: The images show Okabe-Ito colors and "anyplot.ai" branding, but the code file defines community_colors = ["#306998", "#FFD43B", "#FF6B6B"] (Python branding colors, NOT Okabe-Ito) and titles the plot "pyplots.ai". The images appear to have been generated from a different (older/corrected) code version. Per quality criteria, VQ-07 is scored on the code (#306998 = explicit non-compliance).

Score: 76/100

Category	Score	Max
Visual Quality	22	30
Design Excellence	11	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	8	10
Library Mastery	6	10
Total	76	100

Visual Quality (22/30)

• VQ-01: Text Legibility (5/8) — Title at 28px ✓; hub annotations at 14px too small (should be ≥16px); hub annotation text color hardcoded to #333333 (dark-on-dark risk in dark render)
• VQ-02: No Overlap (5/6) — Good force-directed separation; minor risk of hub labels overlapping nearby nodes
• VQ-03: Element Visibility (5/6) — Nodes visible with good size variation; some peripheral nodes are small
• VQ-04: Color Accessibility (2/2) — Three communities clearly distinguishable; CVD-safe
• VQ-05: Layout & Canvas (3/4) — Network fills canvas well; some edge wasted space
• VQ-06: Axis Labels & Title (2/2) — No axis labels appropriate for network graph; title present and centered
• VQ-07: Palette Compliance (0/2) — Code explicitly has community_colors = ["#306998", "#FFD43B", "#FF6B6B"] (Python Blue #306998 is explicitly listed as non-compliant); no ANYPLOT_THEME handling; hardcoded template="plotly_white"; legend uses hardcoded rgba(255,255,255,0.9) background; hub annotations use hardcoded #333333

Design Excellence (11/20)

• DE-01: Aesthetic Sophistication (4/8) — Well-configured with community detection, hub labels, degree-based node sizes; above default but not publication-quality
• DE-02: Visual Refinement (3/6) — No axes (appropriate), semi-transparent edges, node borders. Legend styling is generic (white box not theme-matched)
• DE-03: Data Storytelling (4/6) — Hub labels create focal points; community separation immediately obvious; size variation encodes degree. Good visual hierarchy for a network viz

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct force-directed network graph with physics simulation ✓
• SC-02: Required Features (4/4) — All spec features: 50 nodes, 3 communities, degree-based sizing, intra/inter-community edges, bridge connections ✓
• SC-03: Data Mapping (3/3) — Force-directed layout correct, all nodes and edges visible ✓
• SC-04: Title & Legend (2/3) — Image shows "anyplot.ai" (correct) but code has "pyplots.ai" (wrong branding); legend labels "Engineering/Marketing/Sales" are correct and meaningful

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows all aspects: community structure, degree variation, hub nodes, inter-community bridges, intra-community density ✓
• DQ-02: Realistic Context (5/5) — Corporate social network (Engineering/Marketing/Sales) — realistic, neutral, comprehensible ✓
• DQ-03: Appropriate Scale (4/4) — 50 nodes appropriate for force-directed; 0.3 edge probability gives realistic density ✓

Code Quality (8/10)

• CQ-01: KISS Structure (3/3) — Clean linear: imports → data → FR algorithm → plot → save ✓
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set ✓
• CQ-03: Clean Imports (2/2) — Only numpy and plotly imported ✓
• CQ-04: Code Elegance (1/2) — Clean Pythonic code; FR algorithm implemented manually when networkx could handle it in 2 lines — slightly over-engineered but legitimate
• CQ-05: Output & API (0/1) — Critical: saves as plot.png and plot.html instead of plot-light.png/plot-dark.png/plot-light.html/plot-dark.html; no os.getenv("ANYPLOT_THEME") handling

Library Mastery (6/10)

• LM-01: Idiomatic Usage (3/5) — Correct use of go.Figure, go.Scatter, update_layout. Doesn't leverage plotly's native graph visualization capabilities (Sankey, or combination with networkx pos)
• LM-02: Distinctive Features (3/5) — Uses plotly's interactive hover text (node degree on hover); edge traces with None separators is a classic plotly network pattern; scaleanchor for equal aspect ratio

Score Caps Applied

• None

Strengths

• Excellent data quality: realistic corporate network (Engineering/Marketing/Sales) with proper community structure at right scale (50 nodes)
• Good spec compliance: all required features implemented (degree sizing, community structure, bridge edges, hub identification)
• Clean code structure with deterministic seed
• Effective data storytelling through hub labels and size variation
• Plotly interactivity properly used (hover text showing node degree)

Weaknesses

• VQ-07 / CQ-05 CRITICAL: Code uses Python-branding colors ["#306998", "#FFD43B", "#FF6B6B"] instead of Okabe-Ito ["#009E73", "#D55E00", "#0072B2"]; no ANYPLOT_THEME handling; saves to wrong filenames (plot.png vs plot-light.png/plot-dark.png)
• Hub annotation text color hardcoded to #333333 (dark gray — dark-on-dark in dark theme); should use theme-adaptive INK token
• Hub annotation font size 14px too small; increase to ≥16px
• Title in code says "pyplots.ai" instead of "anyplot.ai"
• Legend background hardcoded to rgba(255,255,255,0.9) — not theme-adaptive; should use ELEVATED_BG token
• DE-01 could be improved: more refined node marker styling (gradient borders, glow for hubs)

Issues Found

1. VQ-07 / CQ-05 CRITICAL: Non-Okabe-Ito palette and missing theme handling
   • Fix: Replace community_colors with ["#009E73", "#D55E00", "#0072B2"]; add ANYPLOT_THEME env var handling with PAGE_BG/INK/ELEVATED_BG tokens; save as plot-{THEME}.png and plot-{THEME}.html
2. SC-04: Title branding mismatch
   • Fix: Change title from "pyplots.ai" to "anyplot.ai"
3. VQ-01: Hub annotation styling not theme-adaptive
   • Fix: Use INK token for annotation font color; increase size to 16px; hub annotation bgcolor should use ELEVATED_BG token
4. DE-01 LOW: Generic legend styling
   • Fix: Apply theme-adaptive legend colors (bgcolor=ELEVATED_BG, font color=INK_SOFT, bordercolor=INK_SOFT)

AI Feedback for Next Attempt

The core logic is excellent — implement ANYPLOT_THEME correctly: (1) read os.getenv("ANYPLOT_THEME", "light"), (2) set PAGE_BG/ELEVATED_BG/INK/INK_SOFT tokens, (3) replace community_colors with ["#009E73", "#D55E00", "#0072B2"], (4) apply INK to hub annotation font color, (5) apply ELEVATED_BG to legend bgcolor and INK_SOFT to legend text/border, (6) save as plot-{THEME}.png and plot-{THEME}.html. Also fix title to say "anyplot.ai" and increase hub annotation font to 16px. For design improvement: consider using INK_SOFT for edge color (instead of hardcoded #AAAAAA) and add a subtle background to hub annotations.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): The plot renders on a warm off-white #FAF8F1 background. Three clearly separated community clusters fill the canvas: a blue cluster (Sales, #0072B2) in the upper center, an orange cluster (Marketing, #D55E00) in the lower left, and a green cluster (Engineering, #009E73) on the right. Node sizes vary by degree — hub nodes are visibly larger. Edges are semi-transparent gray lines (INK_SOFT, opacity 0.35) connecting nodes within and between clusters. "Hub" annotation boxes appear on high-degree nodes (degree ≥ 7), styled with a light elevated background (#FFFDF6) and dark border. The legend in the upper left shows "Teams" header with three community entries in Okabe-Ito color order. The title "network-force-directed · plotly · anyplot.ai" appears in dark #1A1A17 text at the top center. All text is clearly readable against the light background. Legibility verdict: PASS — minor concern is that multiple "Hub" labels cluster together in the dense green and blue communities, reducing their individual readability.

Dark render (plot-dark.png): The same network renders on a warm near-black #1A1A17 background. The layout and data colors are identical to the light render — Engineering green (#009E73), Marketing orange (#D55E00), and Sales blue (#0072B2) are unchanged, confirming only chrome flips. The title appears in light #F0EFE8 text, readable against the dark surface. The legend uses #B8B7B0 text on the dark elevated background (#242420). Hub annotation boxes use light text (#F0EFE8) on the dark elevated background (#242420) with a medium-gray border (#B8B7B0) — no dark-on-dark failure. Grid is hidden (appropriate for network). Edges render as lighter gray (#B8B7B0 at 0.35 opacity), subtle but visible. Legibility verdict: PASS — no text rendered dark-on-dark; chrome adapts correctly in both themes.

Both paragraphs are required. A review that only describes one render is invalid.

Score: 83/100

Category	Score	Max
Visual Quality	24	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	7	10
Total	83	100

Visual Quality (24/30)

• VQ-01: Text Legibility (7/8) — Title 28px, legend 16px, hub annotations 16px all explicitly set; readable in both themes; hub labels slightly crowded in dense clusters but individually readable
• VQ-02: No Overlap (3/6) — Hub "Hub" labels visibly cluster together in the green (Engineering) and blue (Sales) communities where multiple high-degree nodes sit adjacent; labels reference the same cluster region making them hard to distinguish individually
• VQ-03: Element Visibility (5/6) — Nodes clearly visible and size-coded by degree; edges appropriately semi-transparent at 0.35 opacity; both communities and individual nodes distinguishable
• VQ-04: Color Accessibility (2/2) — Okabe-Ito palette is CVD-safe; white node borders (PAGE_BG) add definition against both backgrounds; three-color scheme has strong luminance contrast
• VQ-05: Layout & Canvas (3/4) — Network fills roughly 70% of canvas with balanced margins; some empty space in lower right but overall good proportions
• VQ-06: Axis Labels & Title (2/2) — No axis labels needed (network graph); title correctly formatted
• VQ-07: Palette Compliance (2/2) — Engineering (comm_idx=0) correctly maps to #009E73; Okabe-Ito order followed (#009E73 → #D55E00 → #0072B2); backgrounds #FAF8F1/#1A1A17 correct; data colors identical across both renders

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Multiple thoughtful design choices: degree-scaled node sizes, semi-transparent edges, white node borders, hub annotations with styled boxes; above configured-default territory but hub label clustering detracts from polish; not yet publication-ready
• DE-02: Visual Refinement (4/6) — Grid and spines fully removed; scaleanchor enforces equal aspect ratio; edge opacity reduces visual noise; white node outlines provide definition; hub annotation boxes create localized visual clutter in dense clusters
• DE-03: Data Storytelling (4/6) — Three-community structure immediately visible through color; degree-encoded node sizes emphasize hub nodes; bridge edges between communities are visible; hub annotation boxes reinforce hub identification; over-labeling in dense clusters dilutes the emphasis effect

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Force-directed graph using Fruchterman-Reingold physics simulation implemented correctly from scratch
• SC-02: Required Features (4/4) — Nodes with IDs, edges between nodes, degree-scaled node sizes, community structure; bridge edges for inter-group connectivity
• SC-03: Data Mapping (3/3) — Node positions computed from FR layout; sizes scale linearly with degree (20 + degree×5); communities assigned distinct colors
• SC-04: Title & Legend (3/3) — Title: "network-force-directed · plotly · anyplot.ai" ✓; legend shows "Teams" header with Engineering/Marketing/Sales community names ✓

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows community structure, degree variation, hub nodes, and bridge connections; edge weight as variable thickness not implemented (minor omission)
• DQ-02: Realistic Context (5/5) — Workplace social network (Engineering, Marketing, Sales departments) is a realistic, neutral, comprehensible scenario
• DQ-03: Appropriate Scale (4/4) — 50 nodes (within spec's 20-200 range); 30% intra-community edge probability produces moderately dense clusters; 8 bridge edges create realistic inter-community connectivity

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear: imports → data generation → FR layout → figure → save; no functions or classes
• CQ-02: Reproducibility (2/2) — np.random.seed(42) ✓
• CQ-03: Clean Imports (2/2) — Only os, numpy, plotly.graph_objects; no unused imports
• CQ-04: Code Elegance (2/2) — Manual FR implementation is appropriate given no NetworkX import; None-separator edge pattern is correct and concise; no over-engineering
• CQ-05: Output & API (1/1) — plot-{THEME}.png and plot-{THEME}.html saved correctly ✓

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — None-separator edge trace is the canonical plotly network pattern; separate traces per community for legend is correct; scaleanchor="x" for aspect ratio; hoverinfo="text" with custom text; write_html for interactivity — solidly idiomatic
• LM-02: Distinctive Features (3/5) — Uses plotly-specific hover tooltips (showing node degree), HTML export for full interactivity, scaleanchor for equal aspect ratio; could further leverage hovertemplate for richer hover cards or plotly's annotations with arrowhead for more polished hub pointers

Score Caps Applied

• None applied — VQ-02=3, VQ-03=5, SC-01=5, DQ-02=5, DE-01=5>2 and DE-02=4>2, CQ-04=2

Strengths

• Correct Fruchterman-Reingold physics simulation implemented from scratch with proper repulsion/attraction forces and temperature cooling
• Full theme-adaptive chrome: background, text, legend, hub annotations all use correct tokens in both light and dark renders — no dark-on-dark failures
• None-separator edge trace pattern is the idiomatic plotly approach for network graphs; grouped traces per community correctly drives the legend
• Degree-scaled node sizes elegantly encode network centrality as a visual variable
• Perfect spec compliance: all required features implemented including community structure, degree sizing, and bridge connections

Weaknesses

• Hub annotations (threshold degree ≥ 7) fire on too many nodes in each community, causing multiple "Hub" labels to crowd the same cluster region and overlap each other — reduces legibility and storytelling clarity
• Over-labeling dilutes the visual emphasis: if every hub is labeled, nothing stands out as special; consider labeling only the top 2-3 highest-degree nodes overall, or removing text labels entirely and relying on size alone to convey hub importance

Issues Found

1. VQ-02 OVERLAP: Multiple "Hub" annotation boxes cluster in dense community areas (e.g., 5+ "Hub" labels in the green Engineering cluster)
   • Fix: Raise the hub threshold significantly (e.g., degree ≥ 10 or top-3 overall) OR remove text labels entirely and rely on node size for hub encoding; size already does this job well
2. DE-01 POLISH: Hub annotation clutter is the main factor preventing higher aesthetic score
   • Fix: Fewer, more selective annotations — or replace boxed "Hub" labels with a subtle radial gradient or ring marker on the top-1 or top-3 nodes to highlight them without text noise

AI Feedback for Next Attempt

The core implementation is solid — FR layout, theme tokens, and plotly patterns are all correct. The one remaining issue is hub annotation density: having 10+ "Hub" boxes in a 50-node network overwhelms the viewer and creates label overlap. Fix by either (a) raising the hub threshold to degree ≥ 10 or labeling only the single highest-degree node per community, or (b) removing the text annotation entirely and relying on node size as the hub signal — the size encoding already communicates hub status clearly. This single change would push VQ-02 from 3→6 and unlock higher DE scores.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

Light render (plot-light.png): The plot shows a force-directed graph on a warm off-white background (~#FAF8F1). Three distinct clusters are visible: Engineering (bluish-green #009E73, right side), Marketing (vermillion #D55E00, lower-left), and Sales (blue #0072B2, upper-center). Node sizes vary noticeably by degree — hub nodes are significantly larger, clearly visible. Edges are rendered in a muted gray with 0.35 opacity. Hub annotations ("Engineering hub", "Marketing hub", "Sales hub") appear as small callout boxes with dark text on elevated-background fills. The legend ("Teams") is in the upper-left. Title "network-force-directed · plotly · anyplot.ai" appears in dark text at the top. All text is clearly readable against the light background. Three corner areas of the canvas are somewhat empty due to the scaleanchor aspect-ratio constraint.

Dark render (plot-dark.png): The same plot on a warm near-black background (~#1A1A17). Data colors are identical to the light render — Engineering in #009E73, Marketing in #D55E00, Sales in #0072B2 (only chrome flips). Title text appears in light cream color, clearly readable. Legend text is light-colored against the dark legend box. Hub annotation boxes show dark fill (#242420) with light text (#F0EFE8) — legible and correctly themed. Edges appear as lighter gray against the dark background. No dark-on-dark failures detected. Both renders pass the legibility check.

Both paragraphs are required. A review that only describes one render is invalid.

Score: 89/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	9	10
Total	89	100

Visual Quality (27/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 28px, legend 16px, hub annotations 16px). All text readable in both themes. Minor: hub annotation text at 16px is at the minimum for the canvas size.
• VQ-02: No Overlap (5/6) — Some inherent node-node overlap in dense cluster cores (expected for 50-node force-directed graph). Text elements (annotations, legend) do not overlap.
• VQ-03: Element Visibility (6/6) — Nodes perfectly sized (20 + degree*5), hub nodes visually prominent. Edges visible but appropriately subtle at opacity 0.35.
• VQ-04: Color Accessibility (2/2) — Okabe-Ito palette, CVD-safe, communities distinguishable by both color and position.
• VQ-05: Layout & Canvas (3/4) — Network fills roughly 60% of canvas; scaleanchor constraint creates empty corners. Good proportions, nothing cut off.
• VQ-06: Axis Labels & Title (2/2) — Title in correct format. No axis labels needed for network graph.
• VQ-07: Palette Compliance (2/2) — Engineering (first series) = #009E73 ✓. Okabe-Ito order followed. Correct warm backgrounds in both themes.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Above well-configured default: degree-based node sizing creates intentional visual hierarchy, hub annotations highlight key nodes, Okabe-Ito coloring is clean. Not at publication-level; the hand-rolled FR algorithm adds sophistication but the overall visual composition doesn't reach "strong design."
• DE-02: Visual Refinement (4/6) — Axes fully hidden (appropriate), edge opacity carefully controlled at 0.35, node borders match background color (PAGE_BG) for clean definition. Hub annotation boxes consistently themed. Good refinement above default.
• DE-03: Data Storytelling (4/6) — Visual hierarchy created through three mechanisms: color (communities), size (degree), and labels (hub annotations). Viewer immediately identifies the three-team structure and each team's central connector. Bridge edges between clusters visible.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct force-directed graph with physics simulation layout.
• SC-02: Required Features (4/4) — Node size scales by degree ✓, edge rendering ✓, community structure ✓, Fruchterman-Reingold layout with 200 iterations ✓, hub labels only for key nodes (avoids clutter) ✓.
• SC-03: Data Mapping (3/3) — Force-directed positions correctly computed; communities correctly colored and grouped.
• SC-04: Title & Legend (3/3) — "network-force-directed · plotly · anyplot.ai" ✓. Legend shows all three teams.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows community structure, node degree variation (hub vs. peripheral nodes), intra-community edges, inter-community bridge edges. Full spectrum of force-directed graph features.
• DQ-02: Realistic Context (5/5) — Engineering/Marketing/Sales corporate social network — neutral, business-relevant, realistic. 50-person organization with three departments is perfectly plausible.
• DQ-03: Appropriate Scale (4/4) — 50 nodes (spec range: 20–200). Edge probability 0.3 within communities creates realistic density. 8 bridge edges is plausible for inter-department connections.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — No functions or classes. Clean linear flow: theme tokens → data generation → layout → traces → save.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set at top.
• CQ-03: Clean Imports (2/2) — Only os, numpy, plotly.graph_objects. All used.
• CQ-04: Code Elegance (2/2) — Hand-implemented Fruchterman-Reingold is appropriate (no networkx dependency), community iteration pattern is clean, None-separator edge trace is correct plotly idiom.
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png and plot-{THEME}.html correctly.

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) — Classic plotly network graph pattern: edge trace via None separators, per-community node traces for legend grouping, annotation dicts for hub labels. Expertly idiomatic.
• LM-02: Distinctive Features (4/5) — Uses plotly-specific: interactive hover tooltips (hoverinfo="text" with per-node connection counts), HTML export for full interactivity, None-separator pattern for multi-segment edge traces. Misses animation or more advanced plotly interactivity that would push to 5.

Score Caps Applied

• None — no caps triggered (all gate criteria pass).

Strengths

• Fruchterman-Reingold force-directed algorithm implemented from scratch — no networkx dependency, clean numpy-only approach
• Degree-based node sizing creates meaningful visual hierarchy with hub nodes immediately distinguishable
• Hub annotations (one per community) cleanly label key nodes without cluttering the graph
• Full theme adaptation: all chrome tokens (PAGE_BG, INK, INK_SOFT, ELEVATED_BG) correctly threaded through edges, nodes, annotations, and legend
• None-separator edge trace is the idiomatic plotly pattern for network graphs, correctly applied
• Interactive hover showing node ID and connection count adds genuine value in the HTML output

Weaknesses

• Canvas utilization: scaleanchor constraint causes empty corners; could pass slightly tighter range or adjust margins to better fill canvas
• Hub annotation font size (16px) is at the minimum; bumping to 18px would improve legibility at 4800×2700 resolution
• Design could be more visually striking at DE-01 level — the composition is functional and clean but doesn't reach publication-quality polish

Issues Found

1. VQ-05 MINOR: Empty canvas corners due to scaleanchor aspect-ratio lock. The range [-0.05, 1.05] combined with a 16:9 canvas creates top/bottom dead space.
   • Fix: Adjust to reduce the effective canvas or use a square canvas format (3600×3600) which would suit the graph better
2. VQ-01 MINOR: Hub annotation font at 16px is small for the 4800×2700 output.
   • Fix: Increase hub annotation font size to 18-20px
3. DE-01 DESIGN: The composition is clean but the visual feel is "configured default" rather than "editorial."
   • Fix: Consider a square canvas (better for undirected graphs), slightly larger node border width for definition, or a subtle background annotation layer

AI Feedback for Next Attempt

This is a strong implementation scoring 89/100. The main improvement areas: (1) Use a square canvas format (3600×3600, write_image width=1200, height=1200, scale=3) — force-directed graphs are naturally square/circular and would benefit from a square canvas rather than a 16:9 frame with empty corners. (2) Increase hub annotation font to 18-20px. (3) For DE-01 polish: consider slightly thicker node borders (width=3 instead of 2) and a more deliberate margin layout.

Verdict: REJECTED