Dir-GNN is a machine learning model that enables learning on directed graphs.

Simple graph classes

A demo repo to graph calling and called functions flexing Ghidra's FlatProgramAPI via Pyhidra

Library for building Modular and Asynchronous Graphs with Directed and Acyclic edges (MAGDA)

Node embedding for directed, weighted networks

Chrome extension and Python support scripts to collect data for HTTP Graph

Tool that generates random graph structures under different constraints

This code is the Python adaptation of the MATLAB code found in the paper "A Metric on Directed Graphs and Markov Chains Based on Hitting Probabilities," by Zachary M. Boyd, Nicolas Fraiman, Jeremy Marzuola, Peter J. Mucha, Braxton Osting, and Jonathon Weare.

Code to generate a tweet that quotes itself

Implementation of DirSNN from the paper "Higher-Order Topological Directionality and Directed Simplicial Neural Networks"

Draw graphs corresponding to double occurrence words and study their properties.