Shabnam Khatibi, Jeff Babon, et al.
Growth Factors
Representation and analysis of complex biological and engineered systems as directed networks is useful for understanding their global structure/function organization. Enrichment of network motifs, which are over-represented subgraphs in real networks, can be used for topological analysis. Because counting network motifs is computationally expensive, only characterization of 3- to 5-node motifs has been previously reported. In this study we used a supercomputer to analyze cyclic motifs made of 3-20 nodes for 6 biological and 3 technological networks. Using tools from statistical physics, we developed a theoretical framework for characterizing the ensemble of cyclic motifs in real networks. We have identified a generic property of real complex networks, antiferromagnetic organization, which is characterized by minimal directional coherence of edges along cyclic subgraphs, such that consecutive links tend to have opposing direction. As a consequence, we find that the lack of directional coherence in cyclic motifs leads to depletion in feedback loops, where the number of nodes affected by feedback loops appears to be at a local minimum compared with surrogate shuffled networks. This topology provides more dynamic stability in large networks. © 2008 by The National Academy of Sciences of the USA.
Shabnam Khatibi, Jeff Babon, et al.
Growth Factors
Guillermo A. Cecchi, Marcelo O. Magnasco
Physica A: Statistical Mechanics and its Applications
Shane E. Gordon, Daniel K. Weber, et al.
PLoS Computational Biology
A. Ravishankar Rao, Guillermo A. Cecchi, et al.
IJCNN 2008