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Instead, integrating all the data may widen and deepen the results, offering a better view of the entire system. In the context of network integration, we propose the  algorithm.  assumes a similar network structure, representing latent variables in different network layers of the same system. Therefore, by combining individual edge weights and topological network structures,  first constructs a  that represents the shared information underneath the different layers to provide a global view of the entities that play a fundamental role in the phenomenon of interest. Then, it derives a  for each layer containing peculiar information of the single data type not present in all the others. 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