GEM
awesome-graph-classification
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| GEM | awesome-graph-classification | |
|---|---|---|
| 1 | 1 | |
| 1,265 | 4,698 | |
| - | - | |
| 0.0 | 1.0 | |
| 6 months ago | about 1 year ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | Creative Commons Zero v1.0 Universal |
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GEM
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[D] Why I'm Lukewarm on Graph Neural Networks
Besides, they implemented a fast C++ version of the code that works for much larger graphs. If one searches for ProNE's implementation, they would (hypothetically) find the scikit-style wrapper instead of the fully-functional release. It reminds me of a situation with HOPE, when authors of one survey "implemented" it as naive SVD (https://github.com/palash1992/GEM/blob/master/gem/embedding/hope.py#L68) instead of Jacobi-Davidson generalized solver described in the paper (and literally with code released!!). In the end, I would assume that poor paper was less cited because of that repackaging effort.
awesome-graph-classification
What are some alternatives?
cleora - Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
sursis - A [personal]<-[notebook]->[network]. Complete with custom numerics for constrained Gaussian gravitation physics.
euler - A distributed graph deep learning framework.
karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
PDN - The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)
node2vec-c - node2vec implementation in C++
GAT - Graph Attention Networks (https://arxiv.org/abs/1710.10903)
GraphGPS - Recipe for a General, Powerful, Scalable Graph Transformer