Threshold driven contagion on weighted networks

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Unicomb, Samuel Iñiguez, Gerardo Karsai, Márton 2018-03-16T10:31:47Z 2018-03-16T10:31:47Z 2018-12-01
dc.identifier.citation Unicomb , S , Iñiguez , G & Karsai , M 2018 , ' Threshold driven contagion on weighted networks ' Scientific Reports , vol 8 , no. 1 , 3094 , pp. 1-10 . DOI: 10.1038/s41598-018-21261-9 en
dc.identifier.issn 2045-2322
dc.identifier.other PURE UUID: 45fa9ebd-d659-4e5f-a33e-7747931cc6fa
dc.identifier.other PURE ITEMURL:
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dc.description.abstract Weighted networks capture the structure of complex systems where interaction strength is meaningful. This information is essential to a large number of processes, such as threshold dynamics, where link weights reflect the amount of influence that neighbours have in determining a node's behaviour. Despite describing numerous cascading phenomena, such as neural firing or social contagion, the modelling of threshold dynamics on weighted networks has been largely overlooked. We fill this gap by studying a dynamical threshold model over synthetic and real weighted networks with numerical and analytical tools. We show that the time of cascade emergence depends non-monotonously on weight heterogeneities, which accelerate or decelerate the dynamics, and lead to non-trivial parameter spaces for various networks and weight distributions. Our methodology applies to arbitrary binary state processes and link properties, and may prove instrumental in understanding the role of edge heterogeneities in various natural and social phenomena. en
dc.format.extent 1-10
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Scientific Reports en
dc.relation.ispartofseries Volume 8, issue 1 en
dc.rights openAccess en
dc.subject.other General en
dc.subject.other 113 Computer and information sciences en
dc.title Threshold driven contagion on weighted networks en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Ecole Normale Superieure de Lyon
dc.contributor.department Department of Computer Science
dc.contributor.department School services, SCI
dc.subject.keyword General
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201803161728
dc.identifier.doi 10.1038/s41598-018-21261-9
dc.type.version publishedVersion

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