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 -