Consistent response prediction for multilayer networks on unknown manifolds

TL;DR

This paper introduces a method for predicting responses in multilayer networks lying on unknown one-dimensional manifolds, leveraging a shared subspace model and manifold learning techniques.

Contribution

It proposes a novel algorithm combining a shared subspace network model with isomap for response prediction on unknown manifolds, with theoretical guarantees and practical validation.

Findings

Algorithm accurately predicts responses in simulated data.

Method successfully applied to Drosophila connectome data.

Theoretical analysis confirms consistency of the approach.

Abstract

Our paper deals with a collection of networks on a common set of nodes, where some of the networks are associated with responses. Assuming that the networks correspond to points on a one-dimensional manifold in a higher dimensional ambient space, we propose an algorithm to consistently predict the response at an unlabeled network. Our model involves a specific multiple random network model, namely the common subspace independent edge model, where the networks share a common invariant subspace, and the heterogeneity amongst the networks is captured by a set of low dimensional matrices. Our algorithm estimates these low dimensional matrices that capture the heterogeneity of the networks, learns the underlying manifold by isomap, and consistently predicts the response at an unlabeled network. We provide theoretical justifications for the use of our algorithm, validated by numerical simulations. Finally, we demonstrate the use of our algorithm on larval Drosophila connectome data.