Contextual Graph Markov Model

The official Python implementation for the Contextual Graph Markov Model (CGMM), a deep and generative approach for learning unsupervised encoding of graphs, with a supervised wrapping mechanism for performing graph classification and/or regression.

The code is maintained in the Github of my student Federico Errica, who is to be credited for the implementation. To download the code and the scripts necessary to replicate the experiments in the original paper describing the model, please go here.

The code is provided as is with no warranty and technical support. Please inform the authors of the original paper (details below) if you intend to redistribute the code.


If you find this code useful, please remember to cite:

Bacciu Davide, Errica Federico, Micheli Alessio: Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 2018.