marcelomendoza/IIC3641
GML UC
This project provides Python code examples and resources for a Graph-based Machine Learning (GML) course. It helps students and practitioners understand how to apply GML concepts to various problems. Users can input data represented as graphs and learn techniques to extract insights and build predictive models.
Use this if you are a student or professional looking to learn and apply Graph-based Machine Learning techniques using Python.
Not ideal if you are looking for a plug-and-play application to solve a specific business problem without needing to understand the underlying GML concepts or write code.
Stars
20
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 20, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/marcelomendoza/IIC3641"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
a-r-j/graphein
Protein Graph Library
raamana/graynet
Subject-wise networks from structural MRI, both vertex- and voxel-wise features (thickness, GM...
pykale/pykale
Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for...
dmlc/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.