Maximizing the likelihood of detecting outbreaks in temporal networks
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2020
Typ der Arbeit
Studiengang
Sammlung
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Complex networks and their applications VIII. Volume 2 proceedings of the eighth international conference on complex networks and their applications. Complex Networks 2019
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Studies in Computational Intelligence
Reihennummer
882
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
481-493
Patentnummer
Verlag / Herausgebende Institution
Springer
Verlagsort / Veranstaltungsort
Cham
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Epidemic spreading occurs among animals, humans, or computers and causes substantial societal, personal, or economic losses if left undetected. Based on known temporal contact networks, we propose an outbreak detection method that identifies a small set of nodes such that the likelihood of detecting recent outbreaks is maximal. The two-step procedure involves (i) simulating spreading scenarios from all possible seed configurations and (ii) greedily selecting nodes for monitoring in order to maximize the detection likelihood. We find that the detection likelihood is a submodular set function for which it has been proven that greedy optimization attains at least 63% of the optimal (intractable) solution. The results show that the proposed method detects more outbreaks than benchmark methods suggested recently and is robust against badly chosen parameters. In addition, our method can be used for outbreak source detection. A limitation of this method is its heavy use of computational resources. However, for large graphs the method could be easily parallelized.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
8th International Conference on Complex Networks and their Applications
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
10.12.2019
Enddatum der Konferenz
12.12.2019
Datum der letzten Prüfung
ISBN
978-3-030-36682-7
978-3-030-36683-4
978-3-030-36683-4
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Closed
Lizenz
Zitation
STERCHI, Martin, Cristina SARASUA, Rolf GRÜTTER und Abraham BERNSTEIN, 2020. Maximizing the likelihood of detecting outbreaks in temporal networks. In: Hocine CHERIFI, Sabrina GAITO, José Fernendo MENDES, Esteban MORO und Luis Mateus ROCHA (Hrsg.), Complex networks and their applications VIII. Volume 2 proceedings of the eighth international conference on complex networks and their applications. Complex Networks 2019. Cham: Springer. 2020. S. 481–493. Studies in Computational Intelligence, 882. ISBN 978-3-030-36682-7. Verfügbar unter: https://irf.fhnw.ch/handle/11654/42604