  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.
