"Inference of Edge Correlations in Multilayer Networks"

A new paper of mine came out in final form today. Here are some details.

Title: Inference of Edge Correlations in Multilayer Networks

Authors: A. Roxana Pamfil, Sam D. Howison, and Mason A. Porter

Abstract: Many recent developments in network analysis have focused on multilayer networks, which one can use toencode time-dependent interactions, multiple types of interactions, and other complications that arise in complexsystems. Like their monolayer counterparts, multilayer networks in applications often have mesoscale features,such as community structure. A prominent approach for inferring such structures is the employment of multilayerstochastic block models (SBMs). A common (but potentially inadequate) assumption of these models is thesampling of edges in different layers independently, conditioned on the community labels of the nodes. In thispaper, we relax this assumption of independence by incorporating edge correlations into an SBM-like model. Wederive maximum-likelihood estimates of the key parameters of our model, and we propose a measure of layercorrelation that reflects the similarity between the connectivity patterns in different layers. Finally, we explainhow to use correlated models for edge “prediction” (i.e., inference) in multilayer networks. By incorporating edgecorrelations, we find that prediction accuracy improves both in synthetic networks and in a temporal network ofshoppers who are connected to previously purchased grocery products.