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An Algorithmic Framework for Estimating Rumor Sources With Different Start Times.
- Source :
-
IEEE Transactions on Signal Processing . May2017, Vol. 65 Issue 10, p2517-2530. 14p. - Publication Year :
- 2017
-
Abstract
- We study the problem of identifying multiple rumor or infection sources in a network under the susceptible-infected model, and where these sources may start infection spreading at different times. We introduce the notion of an abstract estimator that, given the infection graph, assigns a higher value to each vertex in the graph it considers more likely to be a rumor source. This includes several of the single-source estimators developed in the literature. We introduce the concepts of a quasi-regular tree and a heavy center, which allows us to develop an algorithmic framework that transforms an abstract estimator into a two-source joint estimator, in which the infection graph can be thought of as covered by overlapping infection regions. We show that our algorithm converges to a local optimum of the estimation function if the underlying network is a quasi-regular tree. We further extend our algorithm to more than two sources, and heuristically to general graphs. Simulation results on both synthetic and real-world networks suggest that our algorithmic framework outperforms several existing multiple-source estimators, which typically assume that all sources start infection spreading at the same time. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 1053587X
- Volume :
- 65
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 124146072
- Full Text :
- https://doi.org/10.1109/TSP.2017.2659643