An application of Reinforced Urn Process to advice network data

We propose a model for network data built as a system of Polya urns. The urns are located on the nodes of the network and their composition is updated through the walk of the Reinforced Urn Process of Muliere, Secchi and Walker (Stochastic Processes and their Applications, 2000). A local preferential attachment scheme is implied, where node popularity positively depends on its strength and on the present position of the urn process. We derive the likelihood of infinitely reinforced random network both for directed and undirected, weighted and unweighted network data. The model is applied to the advice network of students at the Università della Svizzera Italiana.