Locating Contagion Sources in Networks with Partial Timestamps

This paper studies the problem of identifying the contagion source when partial timestamps of a contagion process are available. We formulate the source localization problem as a ranking problem on graphs, where infected nodes are ranked according to their likelihood of being the source. Two ranking algorithms, cost-based ranking (CR) and tree-based ranking (TR), are proposed in this paper. Experimental evaluations with synthetic and real-world data show that our algorithms significantly improve the ranking accuracy compared with four existing algorithms.

[1]  Wuqiong Luo,et al.  Estimating infection sources in a network with incomplete observations , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[2]  Huan Liu,et al.  Seeking provenance of information using social media , 2013, CIKM.

[3]  Lei Ying,et al.  Information source detection in the SIR model: A sample path based approach , 2012, 2013 Information Theory and Applications Workshop (ITA).

[4]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[5]  Chee Wei Tan,et al.  Rooting out the rumor culprit from suspects , 2013, 2013 IEEE International Symposium on Information Theory.

[6]  Martin Vetterli,et al.  Locating the Source of Diffusion in Large-Scale Networks , 2012, Physical review letters.

[7]  Wuqiong Luo,et al.  Finding an infection source under the SIS model , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Wuqiong Luo,et al.  Identifying Infection Sources and Regions in Large Networks , 2012, IEEE Transactions on Signal Processing.

[9]  Yue M. Lu,et al.  A fast Monte Carlo algorithm for source localization on graphs , 2013, Optics & Photonics - Optical Engineering + Applications.

[10]  J. Char.  Generation of Trees, Two-Trees, and Storage of Master Forests , 1968 .

[11]  Christos Faloutsos,et al.  Rise and fall patterns of information diffusion: model and implications , 2012, KDD.

[12]  Chee Wei Tan,et al.  Rumor source detection with multiple observations: fundamental limits and algorithms , 2014, SIGMETRICS '14.

[13]  Devavrat Shah,et al.  Rumors in a Network: Who's the Culprit? , 2009, IEEE Transactions on Information Theory.

[14]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[15]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[16]  Lenka Zdeborová,et al.  Inferring the origin of an epidemy with dynamic message-passing algorithm , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Lei Ying,et al.  Information source detection in the SIR model: A sample path based approach , 2013, ITA.

[18]  Massimo Franceschetti,et al.  Rumor source detection under probabilistic sampling , 2013, 2013 IEEE International Symposium on Information Theory.

[19]  Christos Faloutsos,et al.  Spotting Culprits in Epidemics: How Many and Which Ones? , 2012, 2012 IEEE 12th International Conference on Data Mining.

[20]  P. Pattison,et al.  Cumulated social roles: The duality of persons and their algebras☆ , 1986 .

[21]  Devavrat Shah,et al.  Rumor centrality: a universal source detector , 2012, SIGMETRICS '12.