Testing common invariant subspace of multilayer networks

TL;DR

This paper introduces a hypothesis testing method to determine if multiple layers of a multilayer network share a common invariant subspace, which is crucial for understanding their structural similarities.

Contribution

It proposes a novel Weighted Degree Difference Test for multilayer networks and analyzes its statistical properties and effectiveness through simulations and real data.

Findings

The test effectively distinguishes shared subspaces in multilayer networks.

Simulation results demonstrate the test's satisfactory performance.

Application to real data confirms its practical utility.

Abstract

Graph (or network) is a mathematical structure that has been widely used to model relational data. As real-world systems get more complex, multilayer (or multiple) networks are employed to represent diverse patterns of relationships among the objects in the systems. One active research problem in multilayer networks analysis is to study the common invariant subspace of the networks, because such common invariant subspace could capture the fundamental structural patterns and interactions across all layers. Many methods have been proposed to estimate the common invariant subspace. However, whether real-world multilayer networks share the same common subspace remains unknown. In this paper, we first attempt to answer this question by means of hypothesis testing. The null hypothesis states that the multilayer networks share the same subspace, and under the alternative hypothesis, there exist at least two networks that do not have the same subspace. We propose a Weighted Degree Difference Test, derive the limiting distribution of the test statistic and provide an analytical analysis of the power. Simulation study shows that the proposed test has satisfactory performance, and a real data application is provided.