CRIPAC-DIG/GCA
[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"
This project helps researchers and data scientists working with graph-structured data improve the quality of graph representations. By feeding in raw graph datasets, it outputs more robust and useful feature representations of the nodes and structures within those graphs. This is particularly valuable for machine learning tasks like node classification or link prediction.
181 stars. No commits in the last 6 months.
Use this if you need to create high-quality, general-purpose feature embeddings from your graph data to power downstream predictive models or analyses.
Not ideal if you are looking for a plug-and-play solution for a specific graph task without needing to understand or optimize graph representation learning.
Stars
181
Forks
24
Language
Python
License
MIT
Category
Last pushed
Apr 25, 2024
Commits (30d)
0
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