Extracting the hierarchical organization of complex systems.
Extracting understanding from the growing "sea" of biological and socioeconomic data is one of the most pressing scientific challenges facing us. Here, we introduce and validate an unsupervised method for extracting the hierarchical organization of complex biological, social, and technological networks. We define an ensemble of hierarchically nested random graphs, which we use to validate the method. We then apply our method to real-world networks, including the air-transportation network, an electronic circuit, an e-mail exchange network, and metabolic networks. Our analysis of model and real networks demonstrates that our method extracts an accurate multiscale representation of a complex system.
Sales-Pardo
Marta
M
Department of Chemical and Biological Engineering and Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA.
Guimerà
Roger
R
Moreira
André A
AA
Amaral
Luís A Nunes
LA
D000465:Algorithms; D016000:Cluster Analysis; D004926:Escherichia coli / Q000378:metabolism*; D053858:Metabolic Networks and Pathways; D008660:Metabolism; D018803:Models, Economic; D015233:Models, Statistical; D008962:Models, Theoretical; D011336:Probability; D012923:Social Class; D049490:Systems Biology; D013598:Systems Theory; D014186:Transportation