Author
Pamfil, A
Howison, S
Porter, M
Journal title
Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
DOI
10.1103/PhysRevE.102.062307
Volume
102
Last updated
2023-06-03T04:00:48.323+01:00
Abstract
Many recent developments in network analysis have focused on multilayer networks, which one can
use to encode time-dependent interactions, multiple types of interactions, and other complications
that arise in complex systems. Like their monolayer counterparts, multilayer networks in applications often have mesoscale features, such as community structure. A prominent type of method for
inferring such structures is the employment of multilayer stochastic block models (SBMs). A common (but potentially inadequate) assumption of these models is the sampling of edges in different
layers independently, conditioned on the community labels of the nodes. In this paper, we relax
this assumption of independence by incorporating edge correlations into an SBM-like model. We
derive maximum-likelihood estimates of the key parameters of our model, and we propose a measure of layer correlation that reflects the similarity between connectivity patterns in different layers.
Finally, we explain how to use correlated models for edge “prediction” (i.e., inference) in multilayer
networks. By taking into account edge correlations, prediction accuracy improves both in synthetic
networks and in a temporal network of shoppers who are connected to previously-purchased grocery
products.
Symplectic ID
1133315
Favourite
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Publication type
Journal Article
Publication date
16 Dec 2020
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