Graph theory has always grabbed my attention because it sits at an interesting crossroads of patterns, structure, and practical uses. When I first tried to learn it, I was frustrated by how static the process felt, most tutorials just threw diagrams and complicated pseudocode at me, without letting me actually play or experiment with graphs. That’s when I thought: there has to be a better way, a space where anyone could tinker, ask questions like “what if,” and actually watch these algorithms come alive.

Making learning more hands-on

One thing I quickly realized: you really understand something when you can mess with it and see what happens right away. Want to drag a node to a new spot? Instantly done. Connect two nodes with a click? Easy. Delete an edge and see everything update in real time? That’s the kind of instant feedback that makes concepts click. No more redrawing diagrams every time you want to fiddle with a scenario.

Bringing algorithms out of the abstract

Algorithms often feel like black boxes in textbooks. How exactly does Breadth-First Search explore a graph? Why does Dijkstra’s algorithm always find the shortest path (unless negative weights sneak in)? What does being “strongly connected” really mean when you see it? With the platform, you get to walk through these algorithms on graphs you can edit yourself, complete with animations you can pause or rewind, helpful prompts, and clear-but-not-overwhelming explanations right next to the visuals.

Learning by doing, step-by-step

This isn’t just a sandbox, I wanted a clear path from “I kinda get it” to “I totally own this.” Lessons break topics down by difficulty, come with estimated times, and highlight goals. After each lesson, a practice mode lets you apply what you've learned while it’s still fresh. You can track your progress so you always know what you’ve mastered and what’s coming next. Plus, PDF guides with deeper dives are on the way.

Real-time insights on your work

Old materials left you guessing if your graph had cycles or was connected unless you ran through algorithms manually. Now, stats like node count, edges, density, and connectivity update live as you build or change your graph. If you want a challenge, generate a random graph or pick classic types like trees or bipartite graphs.

Designed for everyone who cares

I added lots of little touches for smoother learning, zoom controls for big graphs, easy import/export, and overlays that highlight node and edge info without clutter. Whether you’re a teacher, student, or just curious, this is for you. It’s part personal journey, part educational tool.

What’s next?

Graph theory is huge, way bigger than this little project. Next up: releasing those PDFs, working on advanced topics like spectral algorithms, and making the platform friendlier and more accessible. Suggestions, feedback, and collaboration would be invaluable, I’m excited to see where we can take this together.

Check it out here: https://learngraphtheory.org/.