The Library
Deterministic ripple-spreading model for complex networks
Tools
Hu, Xiao-Bing, Wang, Ming, Leeson, Mark S., Hines, Evor and Di Paolo, Ezequiel (2011) Deterministic ripple-spreading model for complex networks. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), Vol.83 (No.4). Article: 046123. doi:10.1103/PhysRevE.83.046123 ISSN 1539-3755.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1103/PhysRevE.83.046123
Abstract
This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantagesof 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.
©2011 American Physical Society
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||
Library of Congress Subject Headings (LCSH): | Electric network topology -- Mathematical models | ||||
Journal or Publication Title: | Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) | ||||
Publisher: | American Physical Society | ||||
ISSN: | 1539-3755 | ||||
Official Date: | April 2011 | ||||
Dates: |
|
||||
Volume: | Vol.83 | ||||
Number: | No.4 | ||||
Number of Pages: | 14 | ||||
Page Range: | Article: 046123 | ||||
DOI: | 10.1103/PhysRevE.83.046123 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Funder: | State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, China , International Cooperation Project "Integrated Risk Governance-Models and Modeling" | ||||
Grant number: | 2010DFB20880 (International Cooperation Project "Integrated Risk Governance-Models and Modeling") |
Data sourced from Thomson Reuters' Web of Knowledge
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |