TY - GEN

T1 - A robust information source estimator with sparse observations

AU - Zhu, Kai

AU - Ying, Lei

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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. We adopt the sample path based estimator developed in [1], 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. 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.

AB - 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. We adopt the sample path based estimator developed in [1], 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. 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.

UR - http://www.scopus.com/inward/record.url?scp=84904430579&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904430579&partnerID=8YFLogxK

U2 - 10.1109/INFOCOM.2014.6848164

DO - 10.1109/INFOCOM.2014.6848164

M3 - Conference contribution

AN - SCOPUS:84904430579

SN - 9781479933600

T3 - Proceedings - IEEE INFOCOM

SP - 2211

EP - 2219

BT - IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014

Y2 - 27 April 2014 through 2 May 2014

ER -