mims-harvard/graphml-tutorials
Tutorials for Machine Learning on Graphs
This collection of tutorials helps data scientists and researchers understand and apply machine learning techniques to data that can be represented as graphs. You'll learn how to transform complex, interconnected datasets into a format suitable for graph neural networks and then use these networks to make predictions or uncover patterns. It's designed for anyone working with relational data, such as social networks, molecular structures, or interconnected systems.
230 stars. No commits in the last 6 months.
Use this if you need to analyze relationships and make predictions on data where connections between items are as important as the items themselves.
Not ideal if your data is primarily tabular or image-based, without significant underlying relational structures.
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Jupyter Notebook
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MIT
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Last pushed
Jul 08, 2021
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