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dc.contributor.authorKartun-Giles, AP
dc.contributor.authorBianconi, G
dc.date.accessioned2019-02-12T16:22:11Z
dc.date.available2019-02-12T16:22:11Z
dc.date.issued2019
dc.identifier.citationKartun-Giles, A. and Bianconi, G. (2019). Beyond the clustering coefficient: A topological analysis of node neighbourhoods in complex networks. [online] Chaos, Solitons and Fractals X. Available at: https://arxiv.org/abs/1901.10978 [Accessed 12 Feb. 2019].en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/55288
dc.description.abstractIn Network Science node neighbourhoods, also called ego-centered networks have attracted large attention. In particular the clustering coefficient has been extensively used to measure their local cohesiveness. In this paper, we show how, given two nodes with the same clustering coefficient, the topology of their neighbourhoods can be significantly different, which demonstrates the need to go beyond this simple characterization. We perform a large scale statistical analysis of the topology of node neighbourhoods of real networks by first constructing their clique complexes, and then computing their Betti numbers. We are able to show significant differences between the topology of node neighbourhoods of real networks and the stochastic topology of null models of random simplicial complexes revealing local organisation principles of the node neighbourhoods. Moreover we observe that a large scale statistical analysis of the topological properties of node neighbourhoods is able to clearly discriminate between power-law networks, and planar road networks.en_US
dc.publisherElsevieren_US
dc.subjectphysics.soc-phen_US
dc.subjectphysics.soc-phen_US
dc.subjectcs.SIen_US
dc.titleBeyond the clustering coefficient: A topological analysis of node neighbourhoods in complex networksen_US
dc.typeArticleen_US
dc.rights.holder© Elsevier 2019
pubs.author-urlhttp://arxiv.org/abs/1901.10978v1en_US
pubs.notesNot knownen_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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