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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