TY - JOUR
T1 - Extracting the hierarchical organization of complex systems
AU - Sales-Pardo, Marta
AU - Guimerà, Roger
AU - Moreira, André A.
AU - Nunes Amaral, Luís A.
PY - 2007/9/25
Y1 - 2007/9/25
N2 - 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.
AB - 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.
KW - Cellular metabolism
KW - Complex networks
KW - Multiscale representation
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U2 - 10.1073/pnas.0703740104
DO - 10.1073/pnas.0703740104
M3 - Article
C2 - 17881571
AN - SCOPUS:34848890341
VL - 104
SP - 15224
EP - 15229
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
SN - 0027-8424
IS - 39
ER -