Abstract

Locating sources of diffusion and spreading from minimum data is a significant problem in network science with great applied values to the society. However, a general theoretical framework dealing with optimal source localization is lacking. Combining the controllability theory for complex networks and compressive sensing, we develop a framework with high efficiency and robustness for optimal source localization in arbitrary weighted networks with arbitrary distribution of sources. We offer a minimum output analysis to quantify the source locatability through a minimal number of messenger nodes that produce sufficient measurement for fully locating the sources. When the minimum messenger nodes are discerned, the problem of optimal source localization becomes one of sparse signal reconstruction, which can be solved using compressive sensing. Application of our framework to model and empirical networks demonstrates that sources in homogeneous and denser networks are more readily to be located. A surprising finding is that, for a connected undirected network with random link weights and weak noise, a single messenger node is sufficient for locating any number of sources. The framework deepens our understanding of the network source localization problem and offers efficient tools with broad applications.

Original languageEnglish (US)
Article number170091
JournalRoyal Society Open Science
Volume4
Issue number4
DOIs
StatePublished - Apr 12 2017

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Complex networks
Signal reconstruction
Controllability

Keywords

  • Complex networks
  • Compressive sensing
  • Diffusion sources
  • Locatability
  • Optimal localization

ASJC Scopus subject areas

  • General

Cite this

Optimal localization of diffusion sources in complex networks. / Hu, Zhao Long; Han, Xiao; Lai, Ying-Cheng; Wang, Wen Xu.

In: Royal Society Open Science, Vol. 4, No. 4, 170091, 12.04.2017.

Research output: Contribution to journalArticle

Hu, Zhao Long ; Han, Xiao ; Lai, Ying-Cheng ; Wang, Wen Xu. / Optimal localization of diffusion sources in complex networks. In: Royal Society Open Science. 2017 ; Vol. 4, No. 4.
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