Not logged in.

Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Maximizing the Likelihood of Detecting Outbreaks in Temporal Networks
Organization Unit
Authors
  • Martin Sterchi
  • Cristina Sarasua
  • Rolf Grütter
  • Abraham Bernstein
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published electronically before print/final form (Epub ahead of print)
Language
  • English
ISSN 1860-949X
Page Range 1 - 13
Event Title The 8th International Conference on Complex Networks and their Applications
Event Type conference
Event Location Lisbon
Event Start Date December 10 - 2019
Event End Date December 12 - 2019
Series Name Studies in Computational Intelligence
Place of Publication Heidelberg
Publisher springer
Abstract Text 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 out- break source detection. A limitation of this method is its heavy use of computational resources. However, for large graphs the method could be easily parallelized.
PDF File Download
Export BibTeX
EP3 XML (ZORA)