A principled approach for weighted multilayer network aggregation

TL;DR

This paper introduces a maximum a posteriori estimation algorithm for aggregating weighted multilayer networks into a single layer, preserving core information and validated on biological and social network data.

Contribution

It presents a novel principled method for multilayer network aggregation based on Bayesian estimation, improving information retention over existing approaches.

Findings

The method effectively preserves core network information.

Validation shows improved network quality compared to simple averaging.

The approach is applicable to biological and social multilayer networks.

Abstract

A multilayer network depicts different types of interactions among the same set of nodes. For example, protease networks consist of five to seven layers, where different layers represent distinct types of experimentally confirmed molecule interactions among proteins. In a multilayer protease network, the co-expression layer is obtained through the meta-analysis of transcriptomic data from various sources and platforms. While in some researches the co-expression layer is in turn represented as a multilayered network, a fundamental problem is how to obtain a single-layer network from the corresponding multilayered network. This process is called multilayer network aggregation. In this work, we propose a maximum a posteriori estimation-based algorithm for multilayer network aggregation. The method allows to aggregate a weighted multilayer network while conserving the core information of the layers. We evaluate the method through an unweighted friendship network and a multilayer gene co-expression network. We compare the aggregated gene co-expression network with a network obtained from conflated datasets and a network obtained from averaged weights. The Von Neumann entropy is adopted to compare the mixedness of the three networks, and, together with other network measurements, shows the effectiveness of the proposes method.