Abstract
A disease propagating in a community or a rumor spreading in a social network can be described by a contact network whose nodes are persons or centers of contagion and links heterogeneous relations among them. Suppose that a disease or a rumor originating from a single source among a set of suspects spreads in a network, how to locate this disease/rumor source based on a limited set of observations? We study the problem of estimating the origin of a disease/rumor outbreak: given a contact network and a snapshot of epidemic spread at a certain time, root out the infection source. Assuming that the epidemic spread follows the usual susceptible-infected (SI) model, we introduce an inference algorithm based on sparsely placed observers. We present an algorithm which utilizes the correlated information between the network structure (shortest paths) and the diffusion dynamics (time sequence of infection). The numerical results of artificial and empirical networks show that it leads to significant improvement of performance compared to existing approaches. Our analysis sheds insight into the behavior of the disease/rumor spreading process not only in the local particular regime but also for the whole general network.
This work is supported by the Natural Science Foundation of Zhejiang Province LQ16F030006, National Natural Science Foundation (NNSF) of China under Grant 61503110, 11405059, the General Science Foundation of the Education Department of Zhejiang Province Y201431653, the Startup Foundation of Hangzhou Normal University PF15002004010.
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Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509 (1999)
Zhou, Y.Z., Liu, Z.H., Zhou, J.: Periodic wave of epidemic spreading in community networks. Chin. Phys. Lett. 24(2), 581–584 (2007)
Zhou, J., Liu, Z.H.: Epidemic spreading in communities with mobile agents. Phys. A 388(7), 1228–1236 (2009)
Zhou, J., Xiao, G., Cheong, S.A., Fu, X., Wong, L., Ma, S., et al.: Epidemic reemergence in adaptive complex networks. Phys. Rev. E. 85(3 Pt 2) (2012)
Zhou, J., Chung, N.N., Chew, L.Y., Lai, C.H.: Epidemic spreading induced by diversity of agents’ mobility. Phys. Rev. E 86(2), 026115 (2012)
Zhou, J., Xiao, G., Chen, G.: Link-based formalism for time evolution of adaptive networks. Phys. Rev. E 88(3), 032808 (2013)
Zhou, Y., Xia, Y.: Epidemic spreading on weighted adaptive networks. Phys. A 399(4), 16–23 (2014)
Yao, Y., Zhou, Y.: Epidemic spreading on dual-structure networks with mobile agents. Phys. A 467, 218–225 (2017)
http://www.techinasia.com/china-tweeting-rumors-land-years-jailor-worse/
Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311(5757), 88–90 (2006)
Alcarria, R., Robles, T., Camarillo, G.: Towards the convergence between IMS and social networks. In: International Conference on Wireless and Mobile Communications, pp. 196–201. IEEE Computer Society (2010)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. In: ACM Conference on Electronic Commerce, vol. 1, pp. 228–237. ACM (2006)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 28 June–1 July, pp. 199–208. DBLP (2009)
Pinto, P.C., Thiran, P., Vetterli, M.: Locating the source of diffusion in large-scale networks. Phys. Rev. Lett. 109(6), 068702 (2012)
Zhesi, S., Shinan, C., Wen-Xu, W., Zengru, D., Eugene, S.H.: Locating the source of diffusion in complex networks by time-reversal backward spreading. Phys. Rev. E 93(3), 032301 (2016)
Shah, D., Zaman, T.: Rumors in a network: who’s the culprit? IEEE Trans. Inf. Theory 57(8), 5163–5181 (2011)
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Xu, S., Zhou, Y., Zhang, Z. (2017). Estimating the Origin of Diffusion in Complex Networks with Limited Observations. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_24
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DOI: https://doi.org/10.1007/978-981-10-6805-8_24
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