research-article

Adjusting assortativity in complex networks

Author:

Rong YangAuthors Info & Claims

ACMSE '14: Proceedings of the 2014 ACM Southeast Conference

Article No.: 2, Pages 1 - 5

https://doi.org/10.1145/2638404.2638455

Published: 28 March 2014 Publication History

Abstract

Assortativity has been widely studied for understanding the structure and function of complex networks. Assortative is defined as an association of items with other items having similar characteristics. The research has shown that assortativity has a significant impact on many processes on networks, including information cascades, spreading, congestion relief, longevity, and epidemic thresholds. The degree distribution is also an important factor that affects some of these results. In this paper, we introduce a simple but effective method for adjusting a given network while preserving the degree distribution of the network and, if desired, the connectivity of the network. The algorithm is tested on both theoretical and real-world networks and is supported by detailed empirical results. We illustrate how changing assortativity affects some network properties. The method can be useful for researchers interested in the relationship of assortativity to network structures and the dynamics of processes on networks.

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Cited By

Lagraa SHusák MSeba HVuppala SState ROuedraogo M(2023)A review on graph-based approaches for network security monitoring and botnet detectionInternational Journal of Information Security10.1007/s10207-023-00742-723:1(119-140)Online publication date: 30-Aug-2023
https://dl.acm.org/doi/10.1007/s10207-023-00742-7
Saxena CDoja MAhmad T(2018)Stability of Local Information-Based Centrality Measurements Under Degree Preserving RandomizationsIntelligent Computing and Information and Communication10.1007/978-981-10-7245-1_39(395-403)Online publication date: 20-Jan-2018
https://doi.org/10.1007/978-981-10-7245-1_39

Index Terms

Adjusting assortativity in complex networks

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Highlights
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Published In

cover image ACM Other conferences

ACMSE '14: Proceedings of the 2014 ACM Southeast Conference

March 2014

265 pages

ISBN:9781450329231

DOI:10.1145/2638404

Conference Chair:
Ken Hoganson
Kennesaw State University
,
Program Chair:
Selena He
Kennesaw State University

Copyright © 2014.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2014

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ACM SE '14

ACM SE '14: ACM Southeast Regional Conference 2014

March 28 - 29, 2014

Georgia, Kennesaw

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Cited By

Lagraa SHusák MSeba HVuppala SState ROuedraogo M(2023)A review on graph-based approaches for network security monitoring and botnet detectionInternational Journal of Information Security10.1007/s10207-023-00742-723:1(119-140)Online publication date: 30-Aug-2023
https://dl.acm.org/doi/10.1007/s10207-023-00742-7
Saxena CDoja MAhmad T(2018)Stability of Local Information-Based Centrality Measurements Under Degree Preserving RandomizationsIntelligent Computing and Information and Communication10.1007/978-981-10-7245-1_39(395-403)Online publication date: 20-Jan-2018
https://doi.org/10.1007/978-981-10-7245-1_39

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