ZhuYun97/GraphCLIP
Official implementation of GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs
This project helps data scientists and researchers improve the accuracy of machine learning models when working with large, interconnected datasets that include text, such as research paper networks or social media graphs. It takes existing text-attributed graphs as input and provides a pre-trained model checkpoint that can be directly applied to new, similar graphs to classify or analyze nodes with higher performance, even without extensive retraining. This is ideal for those who need to quickly adapt powerful graph models across different but related datasets.
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Use this if you need to apply advanced graph machine learning to new datasets with text attributes, but don't want to train a complex model from scratch every time.
Not ideal if your datasets do not contain text information associated with their nodes, or if you need to build a model for simple, non-graph structured data.
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Python
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Last pushed
Feb 26, 2025
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