• incremental approach
• machine learning
• structured domain

We present a novel method for unsupervised learning in graphs. It relies on a constructive approach to spread contextual information, which works by stacking layers of probabilistic models. The model is exploited in a supervised setting using an SVM. We describe how structural information can be captured, and empirical evidence shows that our model performs well for tasks involving trees and network data.