Quantifying the Robustness of Complex Networks with Heterogeneous Nodes

The robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world networks consist of nodes that are heterogeneous in natur...

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Autores principales:	Prasan Ratnayake, Sugandima Weragoda, Janaka Wansapura, Dharshana Kasthurirathna, Mahendra Piraveenan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	complex networks network robustness network efficiency node heterogeneity Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/d2600ffa4dcb44ae957be6e209346d4d
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spelling	oai:doaj.org-article:d2600ffa4dcb44ae957be6e209346d4d2021-11-11T18:18:41ZQuantifying the Robustness of Complex Networks with Heterogeneous Nodes10.3390/math92127692227-7390https://doaj.org/article/d2600ffa4dcb44ae957be6e209346d4d2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2769https://doaj.org/toc/2227-7390The robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world networks consist of nodes that are heterogeneous in nature. In this work, we propose a robustness measure called fitness-incorporated average network efficiency, that attempts to capture the heterogeneity of nodes using the ‘fitness’ of nodes in measuring the robustness of a network. Further, we adopt the same measure to compare the robustness of networks with heterogeneous nodes under varying topologies, such as the scale-free topology or the Erdős–Rényi random topology. We apply the proposed robustness measure using a wireless sensor network simulator to show that it can be effectively used to measure the robustness of a network using a topological approach. We also apply the proposed robustness measure to two real-world networks; namely the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">C</mi><msub><mi mathvariant="normal">O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula> exchange network and an air traffic network. We conclude that with the proposed measure, not only the topological structure, but also the fitness function and the fitness distribution among nodes, should be considered in evaluating the robustness of a complex network.Prasan RatnayakeSugandima WeragodaJanaka WansapuraDharshana KasthurirathnaMahendra PiraveenanMDPI AGarticlecomplex networksnetwork robustnessnetwork efficiencynode heterogeneityMathematicsQA1-939ENMathematics, Vol 9, Iss 2769, p 2769 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	complex networks network robustness network efficiency node heterogeneity Mathematics QA1-939
spellingShingle	complex networks network robustness network efficiency node heterogeneity Mathematics QA1-939 Prasan Ratnayake Sugandima Weragoda Janaka Wansapura Dharshana Kasthurirathna Mahendra Piraveenan Quantifying the Robustness of Complex Networks with Heterogeneous Nodes
description	The robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world networks consist of nodes that are heterogeneous in nature. In this work, we propose a robustness measure called fitness-incorporated average network efficiency, that attempts to capture the heterogeneity of nodes using the ‘fitness’ of nodes in measuring the robustness of a network. Further, we adopt the same measure to compare the robustness of networks with heterogeneous nodes under varying topologies, such as the scale-free topology or the Erdős–Rényi random topology. We apply the proposed robustness measure using a wireless sensor network simulator to show that it can be effectively used to measure the robustness of a network using a topological approach. We also apply the proposed robustness measure to two real-world networks; namely the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">C</mi><msub><mi mathvariant="normal">O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula> exchange network and an air traffic network. We conclude that with the proposed measure, not only the topological structure, but also the fitness function and the fitness distribution among nodes, should be considered in evaluating the robustness of a complex network.
format	article
author	Prasan Ratnayake Sugandima Weragoda Janaka Wansapura Dharshana Kasthurirathna Mahendra Piraveenan
author_facet	Prasan Ratnayake Sugandima Weragoda Janaka Wansapura Dharshana Kasthurirathna Mahendra Piraveenan
author_sort	Prasan Ratnayake
title	Quantifying the Robustness of Complex Networks with Heterogeneous Nodes
title_short	Quantifying the Robustness of Complex Networks with Heterogeneous Nodes
title_full	Quantifying the Robustness of Complex Networks with Heterogeneous Nodes
title_fullStr	Quantifying the Robustness of Complex Networks with Heterogeneous Nodes
title_full_unstemmed	Quantifying the Robustness of Complex Networks with Heterogeneous Nodes
title_sort	quantifying the robustness of complex networks with heterogeneous nodes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d2600ffa4dcb44ae957be6e209346d4d
work_keys_str_mv	AT prasanratnayake quantifyingtherobustnessofcomplexnetworkswithheterogeneousnodes AT sugandimaweragoda quantifyingtherobustnessofcomplexnetworkswithheterogeneousnodes AT janakawansapura quantifyingtherobustnessofcomplexnetworkswithheterogeneousnodes AT dharshanakasthurirathna quantifyingtherobustnessofcomplexnetworkswithheterogeneousnodes AT mahendrapiraveenan quantifyingtherobustnessofcomplexnetworkswithheterogeneousnodes
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Quantifying the Robustness of Complex Networks with Heterogeneous Nodes

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