CRIPAC-DIG/GRACE
[GRL+ @ ICML 2020] PyTorch implementation for "Deep Graph Contrastive Representation Learning" (https://arxiv.org/abs/2006.04131v2)
This project helps machine learning researchers and data scientists extract meaningful features from complex network data. By feeding in datasets like scientific paper citations (Cora, CiteSeer, PubMed) or co-authorship networks (DBLP), it generates high-quality representations of the nodes within these graphs. These representations can then be used for tasks like classifying papers or predicting connections.
346 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or data scientist working with graph-structured data and need to learn robust, self-supervised representations for downstream tasks.
Not ideal if you are looking for a plug-and-play solution for graph analytics without diving into model training and evaluation.
Stars
346
Forks
57
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 25, 2024
Commits (30d)
0
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