Thanks to Sebastian Raschka I am able to share this great GitHub overview page of relevant graph classification techniques, and the scientific papers behind them. The overview divides the algorithms into four groups:

1. Factorization
2. Spectral and Statistical Fingerprints
3. Deep Learning
4. Graph Kernels

Moreover, the overview contains links to similar collections on community detection, classification/regression trees and gradient boosting papers with implementations.

As well as a link to relevant graph classification benchmark datasets.

"Awesome Graph Classification" — A collection of graph classification methods, covering embedding, deep learning, graph kernel, and factorization papers with reference implementations https://t.co/ugpL3xSvf1

— Sebastian Raschka (@rasbt) July 16, 2019

• best practices
• deep learning
• machine learning
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• statistics