Classifying complex networks using unbiased local assortativity

Mahendra Piraveenan*, Mikhail Prokopenko, Albert Y. Zomaya

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

18 Citations (Scopus)

Abstract

Assortativity is a network-level measure which quantifies the tendency of nodes to mix with similar nodes in a network. Local assortativity has been introduced as a measure to analyse the contribution of individual nodes to network assortativity. In this paper we argue that there is a bias in the formulation of local assortativity which favours low-degree nodes. We show that, after the bias is removed, local assortativity of a node can be interpreted as a scaled difference between the average excess degree of the node neighbours and the expected excess degree of the network as a whole. Finally, we study the local assortativity profiles of a number of model and real world networks, demonstrating that four classes of complex networks exist: (i) assortative networks with disassortative hubs, (ii) assortative networks with assortative hubs, (iii) disassortative networks with disassortative hubs, and (iv) disassortative networks with assortative hubs.

Original languageEnglish
Title of host publicationArtificial Life XII: Proceedings of the 12th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2010
Place of PublicationDenmark
PublisherSemantic Scholar
Pages329-336
Number of pages8
ISBN (Print)9780262290753
Publication statusPublished - 2010
Externally publishedYes
Event12th International Conference on the Synthesis and Simulation of Living Systems: Artificial Life XII, ALIFE 2010 - Odense, Denmark
Duration: 19 Aug 201023 Aug 2010

Other

Other12th International Conference on the Synthesis and Simulation of Living Systems: Artificial Life XII, ALIFE 2010
CountryDenmark
CityOdense
Period19/08/1023/08/10

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