PolyU IR
 

PolyU Institutional Repository >
Electronic and Information Engineering >
EIE Journal/Magazine Articles >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4825

Title: Scale-free networks which are highly assortative but not small world
Authors: Small, Michael
Xu, Xiaoke
Zhou, Jin
Zhang, Jie
Sun, Junfeng
Lu, Jun-an
Subjects: Complex networks
Network theory (graphs)
Nonlinear dynamical systems
Issue Date: 20-Jun-2008
Publisher: American Physical Society
Citation: Physical review E, statistical, nonlinear, and soft matter physics, June 2008, v. 77, no. 6, 066112, p. 1-7.
Abstract: Uncorrelated scale-free networks are necessarily small world (and, in fact, smaller than small world). Nonetheless, for scale-free networks with correlated degree distribution this may not be the case. We describe a mechanism to generate highly assortative scale-free networks which are not small world. We show that it is possible to generate scale-free networks, with arbitrary degree exponent γ>1, such that the average distance between nodes in the network is large. To achieve this, nodes are not added to the network with preferential attachment. Instead, we greedily optimize the assortativity of the network. The network generation scheme is physically motivated, and we show that the recently observed global network of Avian Influenza outbreaks arises through a mechanism similar to what we present here. Simulations show that this network exhibits very similar physical characteristics (very high assortativity, clustering, and path length).
Description: DOI: 10.1103/PhysRevE.77.066112
Rights: Physical Review E © 2008 The American Physical Society. The Journal's web site is located at http://pre.aps.org/
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/4825
ISSN: 1539-3755 (print)
1550-2376 (online)
Appears in Collections:EIE Journal/Magazine Articles

Files in This Item:

File Description SizeFormat
Small_Scale_Assortativity_World.pdf384.55 kBAdobe PDFView/Open



Facebook Facebook del.icio.us del.icio.us LinkedIn LinkedIn


All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
No item in the PolyU IR may be reproduced for commercial or resale purposes.

 

© Pao Yue-kong Library, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Powered by DSpace (Version 1.5.2)  © MIT and HP
Feedback | Privacy Policy Statement | Copyright & Restrictions - Feedback