Multilayer network alignment based on topological assessment via embeddings

Pietro Cinaglia; Marianna Milano; Mario Cannataro

doi:10.1186/s12859-023-05508-5

Multilayer network alignment based on topological assessment via embeddings

BMC Bioinformatics. 2023 Nov 6;24(1):416. doi: 10.1186/s12859-023-05508-5.

Authors

Pietro Cinaglia¹, Marianna Milano², Mario Cannataro³

Affiliations

¹ Department of Health Sciences, Magna Graecia University, 88100, Catanzaro, Italy. cinaglia@unicz.it.
² Department of Experimental and Clinical Medicine, Magna Graecia University, 88100, Catanzaro, Italy.
³ Data Analytics Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, 88100, Catanzaro, Italy.

Abstract

Background: Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters.

Results: We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively.

Conclusions: DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution.

Keywords: Embeddings; Multilayer networks; Network Alignment; Network analysis; Topological similarity.

MeSH terms

Algorithms*
Reproducibility of Results
Software*