Taking inspiration from the probabilistic principles underlying the topological regularities observed in random networks, the paper presents a simple and efficient Bayesian framework for the classification of (small) labeled random networks.
The proposed “graphical model” relies on a Parzen window estimate of the pairwise vertex–vertex probability distribution under an implicit Markov assumption. Experiments show that, in spite of its simplicity, the approach is at least as accurate as the state-of-the-art machines.
Authors: Edmondo Trentin, Ernesto Di Iorio
Book Title: Neurocomputing