Amit Dhurandhar, Hongyang Li, et al.
Chemical Senses
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.
Amit Dhurandhar, Hongyang Li, et al.
Chemical Senses
Shabnam Khatibi, Hong-Jian Zhu, et al.
BMC Systems Biology
Shane E. Gordon, Daniel K. Weber, et al.
PLoS Computational Biology
Guillermo A. Cecchi, Irina Rish, et al.
NeurIPS 2009