Deep graph node kernels: A convex approach

Nowadays, developing effective techniques able to deal with data coming from structured domains is becoming crucial. In this context kernel methods are the state-of-the-art tool widely adopted in real-world applications that involve learning on structured data. Contrarily, when one has to deal with unstructured domains, deep learning methods represent a competitive, or even better, choice. In this paper we propose a new family of kernels for graphs which exploits a deep representation of the information. Our proposal exploits the advantages of the two worlds. From one side we exploit the potentiality of the state-of-the-art graph kernels. From the other side we develop a deep architecture through a series of stacked kernel pre-image estimators trained in an unsupervised fashion via convex optimization. The hidden layers of the proposed framework are trained in a forward manner and this allows us to avoid the greedy layerwise training of classical deep learning. Results on real world graph datasets confirm the quality of the proposal