Dynamics-based reconstruction of the multilayer structure from an aggregated network

Multi-type interactions are common in complex systems. In many cases, we can only observe whether there is a link between two individuals without knowing the type of the link. The distinction of link types within a complex network is crucial for understanding the dynamics on the network especially when the dynamics behave differently on each type of links. We propose in this paper a network decomposition method using propagation time series, which decomposes an aggregated single-layer network into a multilayer network with each layer consisting of links of the same type. We apply the method to various model networks and real networks and find that it works accurately even when diverse network structural characteristics are present. We also investigate the method's effectiveness and resilience under various restrictions, finding that it is applicable in networks with more than two layers. This work offers an effective and universal framework for untangling the multilayer structure in complex networks.