Locating the Epidemic Source in Complex Networks with Sparse Observers

Epidemic source localization is one of the most meaningful areas of research in complex networks, which helps solve the problem of infectious disease spread. Limited by incomplete information of nodes and inevitable randomness of the spread process, locating the epidemic source becomes a little difficult. In this paper, we propose an efficient algorithm via Bayesian Estimation to locate the epidemic source and find the initial time in complex networks with sparse observers. By modeling the infected time of observers, we put forward a valid epidemic source localization method for tree network and further extend it to the general network via maximum spanning tree. The numerical analyses in synthetic networks and empirical networks show that our algorithm has a higher source localization accuracy than other comparison algorithms. In particular, when the randomness of the spread path enhances, our algorithm has a better performance. We believe that our method can provide an effective reference for epidemic spread and source localization in complex networks.

[1]  A. Barrat,et al.  Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors , 2013, PloS one.

[2]  Feng Ji,et al.  Multiple sources identification in networks with partial timestamps , 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[3]  A Díaz-Guilera,et al.  Self-similar community structure in a network of human interactions. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Li Guo,et al.  Discovering Multiple Diffusion Source Nodes in Social Networks , 2014, ICCS.

[5]  Dongyun Yi,et al.  Locating the source of spreading in temporal networks , 2017 .

[6]  Julie Fournet,et al.  Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers , 2014, Network Science.

[7]  Carsten Wiuf,et al.  Subnets of scale-free networks are not scale-free: sampling properties of networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[10]  Mile Šikić,et al.  Identification of Patient Zero in Static and Temporal Networks: Robustness and Limitations. , 2015, Physical review letters.

[11]  Jianfeng Lu,et al.  Localization of diffusion sources in complex networks with sparse observations , 2018 .

[12]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[13]  Luis E C Rocha,et al.  Information dynamics shape the sexual networks of Internet-mediated prostitution , 2010, Proceedings of the National Academy of Sciences.

[14]  Petter Holme,et al.  Modern temporal network theory: a colloquium , 2015, The European Physical Journal B.

[15]  Wen-Xu Wang,et al.  Multi-source localization on complex networks with limited observers , 2016 .

[16]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[17]  Ciro Cattuto,et al.  What's in a crowd? Analysis of face-to-face behavioral networks , 2010, Journal of theoretical biology.

[18]  M. Vidal,et al.  Effect of sampling on topology predictions of protein-protein interaction networks , 2005, Nature Biotechnology.

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

[20]  Vincenzo Fioriti,et al.  Predicting the sources of an outbreak with a spectral technique , 2012, ArXiv.

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

[22]  Zheng Wang,et al.  Multiple Source Detection without Knowing the Underlying Propagation Model , 2017, AAAI.

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

[24]  Alain Barrat,et al.  Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys , 2015, PloS one.

[25]  Wen-Xu Wang,et al.  Locating the source of diffusion in complex networks by time-reversal backward spreading. , 2016, Physical review. E.

[26]  D. Helbing,et al.  The Hidden Geometry of Complex, Network-Driven Contagion Phenomena , 2013, Science.

[27]  John C. S. Lui,et al.  Information Spreading Forensics via Sequential Dependent Snapshots , 2018, IEEE/ACM Transactions on Networking.

[28]  S. Coulomb,et al.  Gene essentiality and the topology of protein interaction networks , 2005, Proceedings of the Royal Society B: Biological Sciences.

[29]  Li Guo,et al.  Locating multiple sources in social networks under the SIR model: A divide-and-conquer approach , 2015, J. Comput. Sci..