zademn/netsci-labs
(In progress) Network science laboratories. Covers graph theory, random graphs and ML on graphs
This project provides practical, hands-on lab exercises for understanding and applying network science concepts. You'll learn how to analyze complex systems by representing them as graphs, examining characteristics like community structure, and using machine learning to predict behaviors or classify elements within the network. This resource is ideal for data scientists, researchers, or analysts who work with interconnected data and want to deepen their understanding of network analysis.
No commits in the last 6 months.
Use this if you need to learn or apply techniques for understanding relationships and structures within complex data, like social networks, biological interactions, or infrastructure systems.
Not ideal if you're looking for a plug-and-play solution without needing to understand the underlying graph theory and machine learning principles.
Stars
17
Forks
4
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 04, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/zademn/netsci-labs"
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.