Date of Award

6-1-2026

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2026

Department

Computer Science and Engineering

First Advisor

Navid Shaghaghi

Abstract

Mainstream digital note-taking tools such as Notion, Microsoft OneNote, and Evernote organize knowledge around linear documents and rigid folder hierarchies, a structure that strays from the associative, graph-like nature of human semantic memory. Mind-mapping and linked-note systems such as XMind, Obsidian, Roam Research, and Logseq move closer to that associative model but still constrain users to short labels, automatic layouts, or page-centric workflows that underutilize spatial and visual recall. This thesis presents Neuron Notes, a spatial, graph-based note-taking system designed to align digital organization with how people actually form and retrieve ideas. The system is implemented as a three-tier web application: a React 19 single-page client built with TypeScript and Vite, an interactive two-dimensional canvas powered by @xyflow/react with a TipTap rich-text editor, a Django 5 REST Framework backend with token-based authentication, OCR-assisted note import via Pillow and Tesseract, and a MySQL relational store accessed through the Django ORM. Users freely place and connect rich-text nodes, share folders with role-based permissions, and export their maps. Beyond a working system, the thesis introduces a foundation for AI-assisted node and connection generation and expanded collaboration as future work.

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