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The physics of communicability in complex networks

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ELSEVIER
DOI: 10.1016/j.physrep.2012.01.006

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资金

  1. Aihara Innovative Mathematical Modelling Project
  2. Japan Society for the Promotion of Science (JSPS) initiated by the Council for Science and Technology Policy (CSTP)
  3. EPRSC
  4. National Science Foundation [DMS-0810862, DMS-1115692]
  5. Division Of Mathematical Sciences
  6. Direct For Mathematical & Physical Scien [1115692] Funding Source: National Science Foundation

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A fundamental problem in the study of complex networks is to provide quantitative measures of correlation and information flow between different parts of a system. To this end, several notions of communicability have been introduced and applied to a wide variety of real-world networks in recent years. Several such communicability functions are reviewed in this paper. It is emphasized that communication and correlation in networks can take place through many more routes than the shortest paths, a fact that may not have been sufficiently appreciated in previously proposed correlation measures. In contrast to these, the communicability measures reviewed in this paper are defined by taking into account all possible routes between two nodes, assigning smaller weights to longer ones. This point of view naturally leads to the definition of communicability in terms of matrix functions, such as the exponential, resolvent, and hyperbolic functions, in which the matrix argument is either the adjacency matrix or the graph Laplacian associated with the network. Considerable insight on communicability can be gained by modeling a network as a system of oscillators and deriving physical interpretations, both classical and quantum-mechanical, of various communicability functions. Applications of communicability measures to the analysis of complex systems are illustrated on a variety of biological, physical and social networks. The last part of the paper is devoted to a review of the notion of locality in complex networks and to computational aspects that by exploiting sparsity can greatly reduce the computational efforts for the calculation of communicability functions for large networks. (C) 2012 Elsevier B.V. All rights reserved.

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