#Graph-theory
Showing 27 of 27 repositories tagged #graph-theory, ranked by stars
Network Analysis in Python
Graph theory (network) library for visualisation and analysis
Next generation distributed, event-driven, parallel config management!
A library for creating generic graph data structures and modifying, analyzing, and visualizing them.
Playing the game of snake with AI.
A high performance Python graph library implemented in Rust.
:link: C++17 network / graph visualization library - Qt6 / QML node editor.
Graph is a semantic database that is used to create data-driven applications.
Graph algorithms and data structures
Interactive network visualization in Python and Dash, powered by Cytoscape.js
An interactive visualization tool for graph theory
Analyze Data with Pandas-based Networks. Documentation:
A Force Directed Graph Drawing Library
Social network analysis code examples for PyCon 2019 talk
A graph data science library for Rust 🦀 with Python bindings 🐍
Graph algorithms written in GraphBLAS
Code and data for the paper 'Classifying Graphs as Images with Convolutional Neural Networks' (new title: 'Graph Classification with 2D Convolutional Neural Networks')
:books: social networks from novels
BRAPH 2.0 is a comprehensive software package for the analysis and visualization of brain connectivity data, offering flexible customization, rich visualization capabilities, and a platform for collaboration in neuroscience research.
:sparkler: Network/Graph Analysis with NetworkX in Python. Topics range from network types, statistics, link prediction measures, and community detection.
grim brings property graphs to the Nim language. Look around you: everything is a graph!
A realistic simulator of Active Directory domains
Keras implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation". Includes synthetic GED data.
Algorithmic trading using heterogeneous graph neural network and reinforcement learning, pre-alpha
Create and interact with Knowledge Graphs using Generative AI
Autonomous agent networks for task automation that requires multi-step reasoning
Inspired by Matt Parker's video: https://www.youtube.com/watch?v=_-AfhLQfb6w Best time: 1.23s