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.
- Complex networks
- Compressive sensing
- Diffusion sources
- Optimal localization
ASJC Scopus subject areas
FingerprintDive into the research topics of 'Optimal localization of diffusion sources in complex networks'. Together they form a unique fingerprint.
Hu, Z. (Creator), Han, X. (Creator), Lai, Y. (Creator), Wang, W. (Contributor) & Wen-Xu, W. (Contributor), figshare Academic Research System, 2017
Supplementary materials for optimal localization of diffusion sources in complex networks from Optimal localization of diffusion sources in complex networks
Hu, Z. (Creator), Han, X. (Creator), Lai, Y. (Creator) & Wang, W. (Contributor), The Royal Society, 2017
Hu, Z. (Creator), Han, X. (Creator), Lai, Y. (Creator), Wang, W. (Contributor) & Wen-Xu, W. (Contributor), Figshare, 2017