2025/04/27 by Teresa Álvarez San Lázaro, Roger Guimerà, Marta Sales‐Pardo · 1 voice
Physics and Astronomy · Biochemistry, Genetics and Molecular Biology · #Complex Network Analysis Techniques #Bioinformatics and Genomic Networks #DNA and Biological Computing
paper · pdf · doi:10.1038/s41467-025-59077-7
The network alignment problem appears in many areas of science and involves finding the optimal mapping between nodes in two or more networks, so as to identify corresponding entities across networks. We propose a probabilistic approach to the problem of network alignment, as well as the corresponding inference algorithms. Unlike heuristic approaches, our approach is transparent in that all model assumptions are explicit; therefore, it is susceptible of being extended and fine tuned by incorporating contextual information that is relevant to a given alignment problem. Also in contrast to current approaches, our method does not yield a single alignment, but rather the whole posterior distribution over alignments. We show that using the whole posterior leads to correct matching of nodes, even in situations where the single most plausible alignment mismatches them. Our approach opens the door to a whole new family of network alignment algorithms, and to their application to problems for which existing methods are perhaps inappropriate. Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are errorful copies from a blueprint. The method samples the distribution of alignments, improving accuracy and enabling potential applications.