GraphWeave vs Graphica

GraphWeave lets you draw a network by hand and instantly see its geometric and structural properties, computed on the spot and shown in color. Place nodes and links with your finger, and discrete curvature and centrality measures appear right away. ■ Features • Sketch nodes and links directly; drag to arrange them freely • Discrete Ollivier–Ricci curvature for every link • Six node measures: degree, closeness, betweenness and eigenvector centrality, clustering coefficient, and PageRank • Links or nodes are automatically colored on a coolwarm (blue→red) scale, with numeric labels • Save graphs on your device and reopen them anytime to re-analyze • Works fully offline — no account, no network connection ■ Who it's for • Students and teachers learning graph theory and network science • Anyone who wants a hands-on feel for curvature and centrality • Researchers who need a quick look at the structure of a small network ■ Privacy GraphWeave collects no data. Your graphs are stored only on your device and are never sent anywhere. No ads. Metric definitions follow the open-source networkx implementations.

Graphica turns abstract graph theory into something you can actually see. Build your own graphs or load sample ones, then watch nine classic algorithms run step by step — from breadth-first and depth-first search to Dijkstra's shortest path and minimum spanning tree algorithms like Kruskal's and Prim's. FEATURES • 9 graph algorithms, visualized step by step • Pinch-to-zoom canvas for graphs of any size • Synchronized pseudocode panel — see exactly which line is executing • Ready-made sample graphs to explore immediately • Build and edit your own custom graphs • Fully native SwiftUI, built for iOS PRIVACY FIRST Graphica works completely offline. No account, no ads, no analytics, no tracking — your data never leaves your device. Whether you're a computer science student preparing for exams, a developer brushing up before technical interviews, or simply curious how these algorithms work, Graphica makes graph theory click.