THUDM/GraphMAE2
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner in WWW'23
This project helps machine learning engineers improve their graph-based models, specifically for tasks like node classification. It takes graph data, such as scientific citation networks or product recommendation graphs, and outputs a more accurate model for categorizing individual nodes within those graphs. A machine learning practitioner working with graph neural networks would find this useful for pre-training models.
185 stars. No commits in the last 6 months.
Use this if you are developing machine learning models on large graph datasets and need a way to pre-train your models to achieve higher accuracy in classifying nodes.
Not ideal if you are not working with graph data or are looking for a plug-and-play solution without diving into model training and architecture.
Stars
185
Forks
17
Language
Python
License
MIT
Category
Last pushed
Jul 06, 2023
Commits (30d)
0
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