Abstract
The spread of rumors and diseases threatens the development of society, it is of great practical significance to locate propagation source quickly and accurately when rumors or epidemic outbreaks occur. However, the topological structure of online social network changes with time, which makes it very difficult to locate the propagation source. There are few studies focus on propagation source identification in dynamic networks. However, it is usually necessary to know the propagation model in advance. In this paper the label propagation algorithm is proposed to locate propagation source in temporal network. Then the propagation source was identified by hierarchical processing of dynamic networks and label propagation backwards without any underlying information dissemination model. Different propagation models were applied for comparative experiments on static and dynamic networks. Experimental results verify the effectiveness of the algorithm on temporal networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Williams, B.G., Granich, R., Chauhan, L.S., et al.: The impact of HIV/AIDS on the control of tuberculosis in India. Proc. Nat. Acad. Sci. U.S.A. 102(27), 9619–9624 (2005)
Smith, R.D.: Responding to global infectious disease outbreaks: lessons from SARS on the role of risk perception, communication and management. Soc. Sci. Med. 63(12), 3113–3123 (2006)
World Health Organization: Global tuberculosis report 2013. World Health Organization, Geneva, Switzerland (2013)
Shah, D., Zaman, T.: Detecting sources of computer viruses in networks: theory and experiment. In: Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, New York, USA, pp. 203–214 (2010)
Luo, W., Tay, W.P., Leng, M.: How to identify an infection source with limited observations. IEEE J. Sel. Top. Sign. Process. 8(4), 586–597 (2014)
Antulovfantulin, N., Lancic, A., Šmuc, T., Štefančić, H., Šikić, M.: Identification of patient zero in static and temporal networks: robustness and limitations. Phys. Rev. Lett. 114(24), 248701 (2015)
Jiang, J., Wen, S., Yu, S., et al.: Rumor source identification in social networks with time-varying topology. IEEE Trans. Dependable Secure Comput. 99, 1 (2016)
Wang, Z., Wang, C., Pei, J., Ye, X.: Multiple source detection without knowing the underlying propagation model. In: AAAI. AAAI Press, pp. 217–223 (2017)
Fioriti, V., Chinnici, M.: Predicting the sources of an outbreak with a spectral technique. arXiv preprint arXiv:1211.2333 (2012)
Comin, C.H., Da Fontoura, C.L.: Identifying the starting point of a spreading process in complex networks. Phys. Rev. E 84(5), 56105 (2011)
Lokhov, A.Y., Mzard, M., Ohta, H., et al.: Inferring the origin of an epidemic with a dynamic message-passing algorithm. Phys. Rev. E 90(1), 12801 (2014)
Altarelli, F., Braunstein, A., Dall Asta, L., et al.: Bayesian inference of epidemics on networks via belief propagation. Phys. Rev. Lett. 112(11), 118701 (2014)
Prakash, B.A., Vreeken, J., Faloutsos, C.: Spotting culprits in epidemics: how many and which ones? In: IEEE 12th International Conference on Data Mining (ICDM), Brussels, Belgium, vol. 2012, pp. 11–20 (2012)
Huang, Q.: Source locating of spreading dynamics in temporal networks. In: The 26th International Conference. International World Wide Web Conferences Steering Committee (2017)
Lin, Y.-R., Zhu, S., Sundaram, H., Tseng, B.L.: Analyzing communities and their evolutions in dynamic social networks. ACM Trans. Knowl. Discov. Data 3(2), 18 (2009)
Folino, F., Pizzuti, C.: An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans. Knowl. Data Eng. 26(8), 1838–1852 (2014)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. U.S.A. 99, 7821–7826 (2002)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with local and global consistency. Adv. Neural Inform. Process. Syst. 16(16), 321–328 (2004)
Hu, Z.L., Shen, Z., Cao, S., et al.: Locating multiple diffusion sources in time varying networks from sparse observations. Sci. Rep. 8(1) (2018)
Dong, M., Zheng, B., Hung, N., Su, H., Guohui, L.: Multiple rumor source detection with graph convolutional networks. 569–578 (2019). https://doi.org/10.1145/3357384.3357994
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Fan, L., Li, B., Liu, D., Dai, H., Ru, Y. (2020). Identifying Propagation Source in Temporal Networks Based on Label Propagation. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_6
Download citation
DOI: https://doi.org/10.1007/978-981-15-7981-3_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7980-6
Online ISBN: 978-981-15-7981-3
eBook Packages: Computer ScienceComputer Science (R0)