A robust information source estimator with sparse observations

Kai Zhu, Lei Ying

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


Purpose/Background: In this paper, we consider the problem of locating the information source with sparse observations. We assume that a piece of information spreads in a network following a heterogeneous susceptible-infected-recovered (SIR) model, where a node is said to be infected when it receives the information and recovered when it removes or hides the information. We further assume that a small subset of infected nodes are reported, from which we need to find the source of the information. Methods: We adopt the sample path-based estimator developed in the work of Zhu and Ying (arXiv:1206.5421, 2012) and prove that on infinite trees, the sample path-based estimator is a Jordan infection center with respect to the set of observed infected nodes. In other words, the sample path-based estimator minimizes the maximum distance to observed infected nodes. We further prove that the distance between the estimator and the actual source is upper bounded by a constant independent of the number of infected nodes with a high probability on infinite trees. Results: Our simulations on tree networks and real-world networks show that the sample path-based estimator is closer to the actual source than several other algorithms. Conclusions: In this paper, we proposed the sample path-based estimator for information source localization. Both theoretic analysis and numerical evaluations showed that the sample path-based estimator is robust and close to the real source.

Original languageEnglish (US)
Article number3
JournalComputational Social Networks
Issue number1
StatePublished - Dec 1 2014


  • Heterogeneous SIR model
  • Information source detection
  • Sparse observation

ASJC Scopus subject areas

  • Information Systems
  • Modeling and Simulation
  • Human-Computer Interaction
  • Computer Science Applications


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