yifeiacc/COSTA
Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
This project offers a specialized approach to enhance machine learning models that work with graph data, like social networks or molecular structures. It takes your existing graph features and processes them to create richer, more reliable inputs, ultimately improving the accuracy and fairness of your graph-based predictions or classifications. Data scientists and machine learning engineers working with complex graph datasets would find this useful.
No commits in the last 6 months.
Use this if you are building graph-based machine learning models and need a method to improve the quality and reduce bias in your feature augmentation process.
Not ideal if you are not working with graph data or if you need a simple, off-the-shelf data augmentation solution without delving into specific feature-space techniques.
Stars
49
Forks
4
Language
Python
License
—
Category
Last pushed
Jun 07, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/yifeiacc/COSTA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
siapy/siapy-lib
🥷 A Python package for efficient processing of spectral images
yueliu1999/Awesome-Deep-Graph-Clustering
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods...
Tony607/Keras_Deep_Clustering
How to do Unsupervised Clustering with Keras
bourdakos1/CapsNet-Visualization
🎆 A visualization of the CapsNet layers to better understand how it works
loretoparisi/CapsNet
CapsNet (Capsules Net) in Geoffrey E Hinton paper "Dynamic Routing Between Capsules" - State Of the Art