zahta/Graph-Machine-Learning
Course: Graph Machine Learning focuses on the application of machine learning algorithms on graph-structured data. Some of the key topics that are covered in the course include graph representation learning and graph neural networks, algorithms for the world wide web, reasoning over knowledge graphs, and social network analysis.
This course helps graduate students and professionals apply machine learning algorithms to complex, interconnected datasets. It takes raw graph-structured data (like social networks or biological pathways) and teaches how to extract meaningful insights, such as predicting relationships or classifying entities. Anyone working with networked information in fields like computer science, biology, chemistry, or social sciences would benefit from this content.
No commits in the last 6 months.
Use this if you have a background in machine learning and data science and want to gain practical skills in analyzing relationships and patterns within graph-structured data.
Not ideal if you lack foundational knowledge in basic probability, linear algebra, or machine learning concepts, as these are prerequisites for the course material.
Stars
27
Forks
8
Language
Jupyter Notebook
License
—
Category
Last pushed
Jan 24, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/zahta/Graph-Machine-Learning"
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.