{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T03:33:08Z","timestamp":1776396788443,"version":"3.51.2"},"reference-count":61,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:00:00Z","timestamp":1653004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Network alignment is a fundamental task in network analysis. In the biological field, where the protein\u2013protein interaction (PPI) is represented as a graph, network alignment allowed the discovery of underlying biological knowledge such as conserved evolutionary pathways and functionally conserved proteins throughout different species. A recent trend in network science concerns network embedding, i.e., the modelling of nodes in a network as a low-dimensional feature vector. In this survey, we present an overview of current PPI network embedding alignment methods, a comparison among them, and a comparison to classical PPI network alignment algorithms. The results of this comparison highlight that: (i) only five network embeddings for network alignment algorithms have been applied in the biological context, whereas the literature presents several classical network alignment algorithms; (ii) there is a need for developing an evaluation framework that may enable a unified comparison between different algorithms; (iii) the majority of the proposed algorithms perform network embedding through matrix factorization-based techniques; (iv) three out of five algorithms leverage external biological resources, while the remaining two are designed for domain agnostic network alignment and tested on PPI networks; (v) two algorithms out of three are stated to perform multi-network alignment, while the remaining perform pairwise network alignment.<\/jats:p>","DOI":"10.3390\/e24050730","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T13:56:12Z","timestamp":1653054972000},"page":"730","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An Extensive Assessment of Network Embedding in PPI Network Alignment"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1561-725X","authenticated-orcid":false,"given":"Marianna","family":"Milano","sequence":"first","affiliation":[{"name":"Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Gr\u00e6cia, 88100 Catanzaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0048-0457","authenticated-orcid":false,"given":"Chiara","family":"Zucco","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Gr\u00e6cia, 88100 Catanzaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5558-9033","authenticated-orcid":false,"given":"Marzia","family":"Settino","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Gr\u00e6cia, 88100 Catanzaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1502-2387","authenticated-orcid":false,"given":"Mario","family":"Cannataro","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Gr\u00e6cia, 88100 Catanzaro, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"ref_1","first-page":"20120375","article-title":"Network science","volume":"371","year":"2013","journal-title":"Philos. 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