Reviews: RetGK: Graph Kernels based on Return Probabilities of Random Walks

The paper proposes a kernel for graphs able to deal with discrete and continuous labels. In particular, the topology information of a graph is encoded at node level by a return random walk probability vector (each dimension being associated to a different walk length). This probability vector is obtained by classical equations used by random walk kernels for graphs (T. Thanks to that the computational complexity can be reduced since only the entries on the diagonal of the powers of the transition probability matrix need to be computed. This can be done via eigen-decomposition of a rescaled version of the adjacency matrix.