Identifying the diffusion source in complex networks with limited observers

Shuaishuai Xu, Cong Teng, Yinzuo Zhou, Junhao Peng, Yicheng Zhang and Zi-Ke Zhang

Physica A: Statistical Mechanics and its Applications, 2019, vol. 527, issue C

Abstract: Identifying sources of epidemic spreading or rumor diffusion from minority data is of paramount importance in network science with great applied values to the society. However, a general theoretical frame work dealing with source(s) localization is lacking of perfect understanding. Based on limited observers in the network, we study the problem of estimating the origin of a disease/rumor outbreak: given a contact network and a snapshot of epidemic spread at a certain time, root out the infection source. Assuming that the epidemic spread follows the usual susceptible–infected (SI) model, we introduce an inference algorithm based on sparsely placed observers. We present an algorithm which utilizes the correlated information between the network structure (shortest paths) and the diffusion dynamics (time sequence of infection). The numerical results of artificial and empirical networks show that it leads to significant improvement of performance compared to existing approaches. Our analysis sheds insight into the behavior of the disease/rumor spreading process not only in the local particular regime but also for the whole general network.

Keywords: Locating source; Observer nodes; Correlation; Complex network (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307332

DOI: 10.1016/j.physa.2019.121267

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