2020/08/11 by Pietro Hiram Guzzi, Guzzi, Pietro Hiram
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Machine Learning in Bioinformatics #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2008.04581
openalex publication_date 2020/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Network (or Graph) Alignment Algorithms aims to reveal structural similarities among graphs. In particular Local Network Alignment Algorithms (LNAs) finds local regions of similarity among two or more networks. Such algorithms are in general based on a set of seed nodes that are used to grow an alignment. Almost all LNAs algorithms use as seed nodes a set of vertices based on context information (e.g. a set of biologically related in biological network alignment) and this may cause a bias or a data-circularity problem. More recently, we demonstrated that the use of topological information in the choice of seed nodes may improve the quality of the alignments. We used some common approaches based on global alignment algorithms for capturing topological similarity among nodes. In parallel, it has been demonstrated that the use of network embedding methods (or representation learning), may capture the structural similarity among nodes better than other methods. Therefore we propose to use network embeddings to learn structural similarity among nodes and to use such similarity to improve LNA extendings our previous algorithms. We define a framework for LNA.