On the Separability of Aggregated Multilayer Networks

Abstract

Studies are being conducted on analysis methods for multilayer networks. If each layer network of the original multilayer network can be reconstructed from the single-layer network obtained by aggregating the multilayer network, a more detailed analysis of the multilayer network will become possible by using conventional network analysis methods. In this study, we investigate the extent to which the original multilayer network can be reconstructed from the single-layer network with the aggregated multilayer network. Our findings include that semisupervised learning of graph neural networks can reconstruct more than 50% of an original multilayer network from the single-layer network with the aggregated multilayer network.