Deterministic ripple-spreading model for complex networks
Xiao-Bing Hu1,2 ，Ming Wang1 ，Mark S. Leeson2 ，Evor L. Hines2 ，Ezequiel Di Paolo3,4
1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, China
2 School of Engineering, University of Warwick, Coventry, United Kingdom
3 Ikerbasque, Basque Science Foundation, Centre for Research on Life, Mind and Society, University of the Basque Country, San Sebastian,Spain
4 The Centre for Computational Neuroscience and Robotics, Department of Informatics, University of Sussex, Brighton, United Kingdom
Abstract: This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.
Keywords: network model，ripple-spreading phenomenon，advantages of the model
Published in PHYSICAL REVIEW E 83, 046123 (2011)
