Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2007-02-06
Proceedings of the National Academy of Sciences, Vol. 104, 7746 (2007)
Physics
Condensed Matter
Disordered Systems and Neural Networks
8 pages, 6 figures
Scientific paper
10.1073/pnas.0700250104
Transport is an important function in many network systems and understanding its behavior on biological, social, and technological networks is crucial for a wide range of applications. However, it is a property that is not well-understood in these systems and this is probably due to the lack of a general theoretical framework. Here, based on the finding that renormalization can be applied to bio-networks, we develop a scaling theory of transport in self-similar networks. We demonstrate the networks invariance under length scale renormalization and we show that the problem of transport can be characterized in terms of a set of critical exponents. The scaling theory allows us to determine the influence of the modular structure on transport. We also generalize our theory by presenting and verifying scaling arguments for the dependence of transport on microscopic features, such as the degree of the nodes and the distance between them. Using transport concepts such as diffusion and resistance we exploit this invariance and we are able to explain, based on the topology of the network, recent experimental results on the broad flow distribution in metabolic networks.
Gallos Lazaros K.
Havlin Shlomo
Makse Hernan A.
Song Chaoming
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