When collecting several data sets and heterogeneous data types on a given phenomenon of interest, the individual analysis of each data set will provide only a particular view of such phenomenon. 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 INet algorithm. INet 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, INet first constructs a Consensus Network 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 Case Specific Network for each layer containing peculiar information of the single data type not present in all the others. We demonstrated good performance with our method through simulated data and detected new insights by analyzing biological and sociological datasets.
