danielegrattarola/GNCA
Code for "Learning Graph Cellular Automata" (NeurIPS 2021).
This project provides code to explore and experiment with Graph Cellular Automata (GCA), which are systems that can learn to evolve and adapt on complex network structures. It takes raw graph data or simulated environments (like 'Boids' flocking behavior or Voronoi patterns) as input and outputs trained GCA models and visualizations of their evolving states. Researchers and students working in computational intelligence, complex systems, or machine learning for graph-structured data would use this.
No commits in the last 6 months.
Use this if you are a researcher or student interested in understanding, training, and visualizing how Graph Cellular Automata can model dynamic processes on networks.
Not ideal if you are looking for a plug-and-play solution for real-world graph prediction tasks without a deep interest in the underlying GCA mechanics.
Stars
75
Forks
18
Language
Python
License
—
Category
Last pushed
Sep 16, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/danielegrattarola/GNCA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
fangwei123456/spikingjelly
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
neuromorphs/NIR
Neuromorphic Intermediate Representation reference implementation
BindsNET/bindsnet
Simulation of spiking neural networks (SNNs) using PyTorch.
norse/norse
Deep learning with spiking neural networks (SNNs) in PyTorch.
jeshraghian/snntorch
Deep and online learning with spiking neural networks in Python