Contextual Graph Markov Model
CGMM is a generative approach to learning contexts in graphs. It combines information diffusion and local computation through the use of a deep architecture and stationarity assumptions. The model does NOT preprocess the graph into a fixed structure before learning. Instead, it works with graphs of any size and shape while retaining scalability. Experiments show that this model works well compared to expensive kernel methods that extensively analyse the entire input structure in order to extract relevant features. In contrast, CGMM extract as more abstract features as the architecture is built (incrementally).
This repo is forked by the repository of @diningphil (https://github.com/diningphil/CGMM). The code was modified and used for vectorizing and training models by the project https://github.com/Djack1010/GrapPa.
If you happen to use or modify this code, please remember to cite the foundation papers:
Bacciu Davide, Errica Federico, Micheli Alessio: Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. To Appear in the Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Forthcoming.
and the authors of this repository:
- Giacomo Iadarola - contributor - Djack1010
- Federico Errica - contributor original repo - diningphil