Data Mining and Knowledge Engineering

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Rumor Source Identification in Social Network with Time-Varying Topology


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1 Adhiyamaan College of Engineering, Hosur, India
     

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Identifying rumor source in social networks place a critical role in limiting the damage caused by them through the timely isolation of the source. The trouble of rumor source identification in time- varying social networks that can be minimized to a series of static networks by introducing a time-integrating window. First, reduce the time-varying networks to a series of static networks by introducing a time integrating window. Second, rather than of inspecting every individual in traditional techniques, adopt a reverse dissemination strategy to specify a set of suspects of the real rumor source. The Third is to determine the real source from the suspects. Information that spread through social networks can carry a lot of false claims.


Keywords

ML based Method, Rationale, Wavefront.
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  • Agaskar and Y. M. Lu, “A fast Carlo Monte algorithm for source localization graphs,” in SPIE Optical Engineering and Applications. International Society for Optics and Photonics, 2013.

  • Jiaojiao Jiang, Sheng Wen, Shui Yu, Yang Xiang, and Wanlei Zhou, "RumorSource Identification in Social Networks with Time-varying Topology", 2016.

  • M.P. Viana, D.R. Amancio, and L.d.F Costa,”On time-varying collaboration networks,” Journal of Infometrics, vol.7, no.2, pp.371-378, 2013.

  • W. Zang, P. Zang, C. Zhou, and L. Guou," Discovering multiple diffusion source nodes in social networks, "Procedia Computer Science, vol.29, pp.443-452, 2014.

  • J.Jiang, S.Wen, S.Yu, Y.Xiang and W.Zhou “k-center: An Approach to the multi-source identification of information diffusion", 2015.

  • B. Ribeiro, N. Perra, and A. Baronchelli, "Quantifying the temporal resolution on time- networks," scientific reports, vol.3, 2013.

  • K. Zhu and L. Yiang," information source detection in SIR model: A sample path based approach,” in Information Theory and Applications, Workshop (ITA), pp.1-9, 2013

  • P.C. Pinto, P. Thiran, and M. Vetterli, "Locating the source of diffusion in large- scale networks, "Phys. Rev.Lett., vol.109, Aug, 2012.

  • Qiao Zhang, Shuiyuan Zhang, Jian Dong, Jinhua Xiong, and Xueqi Cheng,” Automatic Detection of Rumor on Social Network," 2015.

  • H. Okamura, K. Tateishi, and T. Doshi, “Statistical inference of computer virus propagation using non-homogeneous Poisson processes,” Proc. 18th IEEE International Symposium on Software Reliability, vol. 5-9, pp. 149 – 158, (2012).


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  • Rumor Source Identification in Social Network with Time-Varying Topology

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Authors

Swetha
Adhiyamaan College of Engineering, Hosur, India
Tamilselvi
Adhiyamaan College of Engineering, Hosur, India
Vinitha
Adhiyamaan College of Engineering, Hosur, India
Gracy Theresa
Adhiyamaan College of Engineering, Hosur, India

Abstract


Identifying rumor source in social networks place a critical role in limiting the damage caused by them through the timely isolation of the source. The trouble of rumor source identification in time- varying social networks that can be minimized to a series of static networks by introducing a time-integrating window. First, reduce the time-varying networks to a series of static networks by introducing a time integrating window. Second, rather than of inspecting every individual in traditional techniques, adopt a reverse dissemination strategy to specify a set of suspects of the real rumor source. The Third is to determine the real source from the suspects. Information that spread through social networks can carry a lot of false claims.


Keywords


ML based Method, Rationale, Wavefront.

References