Visualizing Graphs with Raku

LLM summary report

Summary

Anton Antonov’s presentation “graph neat” (November 13, 20124) shows Raku examples including an undirected triangular grid graph with vertices colored by degree and the corresponding adjacency and incidence matrices. He defines “NE examples” as concise Raku code that produce compelling visual or textual outputs, and demonstrates using the graph package with JavaScript D3 and GraphViz DOT to generate SVG or notebook visualizations, including deriving bipartite colorings and highlight lists for grid graphs. He also converts undirected grids into randomly directed graphs to visualize connected components, discusses DOT vertex/node shape options and preamble styling, and presents a collage of forty star graphs with eight to sixteen rays to show layout and styling differences between JavaScript and DOT renderings.

Topics

theme	content
Title and introduction	Presentation 'Graph Neat'; Anton Antonov; November 13th 20124.
Triangular grid undirected example	Undirected triangular grid graph with vertices colored by degree.
Adjacency and incidence matrices	Adjacency matrix symmetric; incidence matrix shows vertex-edge relationships, two nonzero entries per column.
Definition of neat examples	Concise Raku code producing compelling visual/text outputs showcasing modules and capabilities.
Tools and visualization libraries	Using Raku graph package, JavaScript D3 for visualization, and Graphviz dot for SVG.
Bipartite property and code	Grid graphs are bipartite; derive bipartite coloring using classify, highlights, and vertex coordinates.
Directed conversion and connected components	Randomly orient undirected grid to directed; colored components show asymmetric reachability.
Jupyter and rendering	JavaScript and SVG renderings displayed in Jupyter notebooks using Raku chatbook framework.
DOT language and node shapes	Use dot language, random node shapes; 'node' versus 'vertex' terminology explained.
Collage of star graphs	Forty star graphs with eight to sixteen rays; selected ones colored; code possibly LLM-generated.
DOT DSL advantages and conclusion	Dot DSL permits style preamble for concise, consistent plots; closing remarks and thanks.

Mind map

mindmap “Graph Neat” “Presenter” “Anton Antonov” “Date” “November 13th 20124” “Tools” “Raku” “graph package” “Visualization” “JavaScript (D3 / d3.js)” “Graphviz (DOT -> SVG)” “Examples” “Example 1: Triangular grid (undirected)” “Vertices colored by degree” “Adjacency matrix — vertex-to-vertex (symmetric)” “Incidence matrix — vertex-to-edge (cols have 2 nonzeros)” “Example 2: Grid graph (bipartite)” “Two vertex sets (no intra-set edges)” “Verify bipartite; derive partition coloring” “Use classify / highlight lists” “Leverage known vertex coordinates” “Example 3: Directed random orientation” “Start from undirected grid; orient edges randomly” “Find connected components (directed reachability)” “Components colored; asymmetric reachability” “Visualize via JS or DOT; random node shapes” “Example 4: Collage of star graphs” “40 stars with 8–16 rays” “Subset colored; interactive manipulation” “Code possibly LLM-generated” “DOT gives more regular layout; use preamble for style” “DOT vs JS” “DOT: DSL, preamble, reproducible SVG” “JS: D3 rendering, interactive in notebooks”

Thinking hats

Hat	Perspective	Feedback	New ideas
Gray hat	Cynicism and skepticism — call out obfuscation, glossing, hidden assumptions and sloppy claims with a sharp, darkly humorous tone.	Nice-looking demos, but there are a lot of small lies and sloppy shortcuts hiding behind the polish. The transcript itself is messy (e.g., “20124” for the year), you switch terminology mid-sentence (“vertex” vs “node”), and you casually drop “the code was generated using large language models” without validating or attributing what was actually changed. That combination invites hallucinated outputs, brittle reproducibility, and untested runtime assumptions (random directedization, coordinate availability, DOT installation on users’ systems). Your descriptions also hedge and handwave—“it’s almost straightforward,” “I didn’t…show”—which reads like someone covering for missing reproducibility and test coverage. There’s also an implicit claim that the visualizations are pedagogically clear; yet without interactive labels, algorithmic traces, or deterministic seeds, the visuals are interesting but not reliably informative. Finally, the presentation risks sounding like marketing for “look at my cool graphs” rather than a technical demonstration with reproducible, testable claims.	Add provenance and deterministic seeds for randomized steps (so outputs are verifiable), perform a concise audit of LLM-generated code before presenting (highlight what was changed), add a tiny reproducibility checklist including DOT version and JS dependencies
Yellow hat	Benefits and creativity — optimistic, imaginative, focuses on what can succeed and grow from the idea.	The project is nicely positioned: Raku + graph package + D3/DOT gives you a flexible toolchain for beautiful, varied visualizations (grid graphs, bipartite coloring, directed components, star-collages). Using both DOT and interactive JavaScript covers static high-quality outputs (SVG from DOT) and dynamic, exploratory experiences (D3). The examples already show teaching value (degree coloring, bipartiteness, reachability in directed graphs) and aesthetic appeal (star collage, random shapes). Showing multiple rendering backends is a strength—some audiences prefer polished SVG, others want live manipulation. The mix of algorithmic insight (connected components, bipartite checks) and visual appeal can make this a standout demo suite for Raku and graph pedagogy.	Build interactive notebooks/demos with sliders for grid size and randomness, create a “gallery” of canonical graph demos (grid, triangular, stars, cliques) with one-click exports to SVG/DOT, add short guided exercises (e.g., “toggle edge directions and watch components split/merge”) to turn the presentation into a teachable module