guoji-fu/pGNNs
[ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks
This project provides an implementation of p-Laplacian Based Graph Neural Networks, a specialized machine learning model for analyzing data structured as graphs. It takes graph-structured data (like networks of connections) and outputs classifications or predictions based on the relationships within that data. This is primarily for machine learning researchers and practitioners who develop and test advanced graph-based algorithms.
No commits in the last 6 months.
Use this if you are a machine learning researcher working with graph-structured data and need to implement or benchmark a specific type of graph neural network from a published academic paper.
Not ideal if you are looking for an off-the-shelf solution for general graph analysis, as this requires familiarity with machine learning frameworks and academic concepts.
Stars
27
Forks
5
Language
Python
License
MIT
Category
Last pushed
Aug 26, 2025
Commits (30d)
0
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