LineRegression vs GraphWeave

LREG – Linear Regression Equation Generator. Developed by Prof. Carlos França, the LREG is a free educational app created within the Educational Technologies Research Group (GTED) at the Federal University of Fronteira Sul (UFFS), Brazil. It provides an interactive environment to learn, apply, and understand the linear regression equation in a simple, intuitive, and precise way. The LREG allows users to input their own X and Y values, automatically generate the regression line equation (Y = A×X + B), display the correlation coefficient (R), and explore the linear relationship between variables — all in an instructive format that encourages mathematical reasoning. More than an app, LREG represents the GTED/UFFS commitment to pedagogical innovation and the promotion of accessible educational technologies for everyone. It is a non-commercial project based on the philosophy of “doing good for the sake of good”, emphasizing human learning and ethical technology.

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.