Hidden network generating rules from partially observed complex networks

Abstract

Understanding heterogeneous topological structures in real-world complex networks is challenged by the difficulty of describing their multifractal nature and inferring their generator rules. Here, the authors present a weighted multifractal graph model as a generative approach for studying the structural properties of complex networks in realistic scenarios where only partial observational data is available or the input network is noisy, and demonstrate it on biological networks.