The spindle approximation of network epidemiological modeling

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMou, Jianhongen_US
dc.contributor.authorDai, Bitaoen_US
dc.contributor.authorTan, Suoyien_US
dc.contributor.authorHolme, Petteren_US
dc.contributor.authorLehmann, Suneen_US
dc.contributor.authorLiljeros, Fredriken_US
dc.contributor.authorLu, Xinen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProfessorship Holme Petteren
dc.contributor.groupauthorComputer Science Professorsen
dc.contributor.groupauthorComputer Science - Complex Systems (Cxsys)en
dc.contributor.organizationNational University of Defense Technologyen_US
dc.contributor.organizationTechnical University of Denmarken_US
dc.contributor.organizationStockholm Universityen_US
dc.date.accessioned2024-05-22T05:50:06Z
dc.date.available2024-05-22T05:50:06Z
dc.date.issued2024-05-03en_US
dc.descriptionPublisher Copyright: © 2024 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.
dc.description.abstractUnderstanding the dynamics of spreading and diffusion on networks is of critical importance for a variety of processes in real life. However, predicting the temporal evolution of diffusion on networks remains challenging as the process is shaped by network topology, spreading non-linearities, and heterogeneous adaptation behavior. In this study, we propose the ‘spindle vector’, a new network topological feature, which shapes nodes according to the distance from the root node. The spindle vector captures the relative order of nodes in diffusion propagation, thus allowing us to approximate the spatiotemporal evolution of diffusion dynamics on networks. The approximation simplifies the detailed connections of node pairs by only focusing on the nodal count within individual layers and the interlayer connections, seeking a compromise between efficiency and complexity. Through experiments on various networks, we show that our method outperforms the state-of-the-art on BA networks with an average improvement of 38.6% on the mean absolute error. Additionally, the predictive accuracy of our method exhibits a notable convergence with the pairwise approximation approach with the increasing presence of quadrangles and pentagons in WS networks. The new metric provides a general and computationally efficient approach to predict network diffusion problems and is of potential for a large range of network applications.en
dc.description.versionPeer revieweden
dc.format.extent21
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMou, J, Dai, B, Tan, S, Holme, P, Lehmann, S, Liljeros, F & Lu, X 2024, ' The spindle approximation of network epidemiological modeling ', New Journal of Physics, vol. 26, no. 4, 043027, pp. 1-21 . https://doi.org/10.1088/1367-2630/ad4050en
dc.identifier.doi10.1088/1367-2630/ad4050en_US
dc.identifier.issn1367-2630
dc.identifier.otherPURE UUID: 4c54e598-cdee-477b-a69e-ac159f845834en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/4c54e598-cdee-477b-a69e-ac159f845834en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85192226676&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/146372585/The_spindle_approximation_of_network_epidemiological_modeling.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/127900
dc.identifier.urnURN:NBN:fi:aalto-202405223505
dc.language.isoenen
dc.publisherInstitute of Physics Publishing
dc.relation.ispartofseriesNew Journal of Physics
dc.relation.ispartofseriesVolume 26, issue 4, pp. 1-21
dc.rightsopenAccessen
dc.subject.keywordnetwork diffusionen_US
dc.subject.keywordpairwise approximationen_US
dc.subject.keywordpropagation approximationen_US
dc.subject.keywordspindle vectoren_US
dc.titleThe spindle approximation of network epidemiological modelingen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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