Math Knowledge Graph

A deployable, Wiki-style visualization of essential university mathematics covering calculus, linear algebra, probability & statistics, optimization, machine learning / deep learning / LLMs, quantitative finance, and high-performance computing.

All content is written in English and sourced from authoritative textbooks, papers, and courses.

Features

• Knowledge graph: interactive Cytoscape.js graph with prerequisite, related, and application edges.
• Wiki-style pages: one page per topic with key points, formulas, code examples, tags, and references.
• Math rendering: TeX/MathJax for formulas.
• Code highlighting: Prism.js with line numbers and Python syntax support.
• Search: live search across titles, tags, and summaries.
• Responsive: works on desktop and mobile.
• GitHub Pages ready: static HTML/CSS/JS output in docs/.

Project Structure

.
├── src/
│   ├── data/topics.js       # Knowledge graph nodes, edges, and content
│   ├── assets/
│   │   ├── style.css        # Minimalist stylesheet
│   │   └── app.js           # Graph, search, and page interactions
├── docs/                    # Generated static site (deploy to GitHub Pages)
├── build.js                 # Node.js build script
└── README.md

Build

Requires Node.js (managed runtime preferred).

node build.js

To build for a GitHub Pages project site (repo served under a subpath), set BASE_PATH:

# Example: repo named "math-knowledge-graph"
BASE_PATH=/math-knowledge-graph/ node build.js

Deploy to GitHub Pages

Option 1: Deploy from docs/ folder on main branch

1. Push this repository to GitHub.
2. Go to Settings → Pages.
3. Under Build and deployment, select Deploy from a branch.
4. Choose main branch and /docs folder, then click Save.

Option 2: GitHub Actions

Enable the included workflow in .github/workflows/deploy.yml. It builds the site and deploys it to GitHub Pages on every push to main.

Local Preview

# Quick static server
cd docs
python -m http.server 8000

Then open http://localhost:8000.

Content Sources

Content is based on authoritative references including:

• Calculus and Calculus on Manifolds by Michael Spivak
• Introduction to Linear Algebra by Gilbert Strang
• Linear Algebra Done Right by Sheldon Axler
• Probability and Measure by Patrick Billingsley
• All of Statistics by Larry Wasserman
• Convex Optimization by Stephen Boyd and Lieven Vandenberghe
• Numerical Optimization by Jorge Nocedal and Stephen J. Wright
• Deep Learning by Goodfellow, Bengio, and Courville
• Attention Is All You Need by Vaswani et al.
• Stochastic Calculus for Finance by Steven E. Shreve
• Portfolio Selection by Harry Markowitz
• Numerical Linear Algebra by Trefethen and Bau
• Fourier Analysis by Stein and Shakarchi

See each topic page for specific references.

License

MIT