Locating multiple diffusion sources in time varying networks from sparse observations

Zhao Long Hu, Zhesi Shen, Shinan Cao, Boris Podobnik, Huijie Yang, Wen Xu Wang, Ying-Cheng Lai

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Data based source localization in complex networks has a broad range of applications. Despite recent progress, locating multiple diffusion sources in time varying networks remains to be an outstanding problem. Bridging structural observability and sparse signal reconstruction theories, we develop a general framework to locate diffusion sources in time varying networks based solely on sparse data from a small set of messenger nodes. A general finding is that large degree nodes produce more valuable information than small degree nodes, a result that contrasts that for static networks. Choosing large degree nodes as the messengers, we find that sparse observations from a few such nodes are often sufficient for any number of diffusion sources to be located for a variety of model and empirical networks. Counterintuitively, sources in more rapidly varying networks can be identified more readily with fewer required messenger nodes.

Original languageEnglish (US)
Article number2685
JournalScientific Reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • General

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