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Inferring Diffusion Network on Incomplete Cascade Data

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Web-Age Information Management (WAIM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9658))

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Abstract

Inferring the diffusion network based on observed cascades is fundamental and of interest in the field of information diffusion on the network. All the existing methods which aim to infer the network assume that the cascades are complete without any missing nodes. In real world, not every infection between nodes can be easily observed or acquired. As a result, there are some missing nodes in the real cascades, which indicates that the observed cascades are incomplete and makes more challenges for solving this problem. Being able to recover the incomplete cascades is critical to us since inferred network based on the incomplete cascades can be inaccurate. In this paper, we tackle the problem by developing a two-stage framework, which finds the paths that contain the missing nodes at first and then estimate the location and infection time of missing nodes. Experiments on real and synthetic data show the accuracy of our algorithm to finding the missing node on the network, as well as the infection time of the missing nodes.

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Acknowledgement

This work was supported by the National Science and Technology Support Plan(2014B AG01B02), the National Natural Science Foundation of China (61572041), the National High Technology Research and Development Program of China (2014AA015103), and Beijing Natural Science Foundation (4152023).

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Authors and Affiliations

  1. Key Lab of Machine Perception (MOE), Peking University, Beijing, China

    Peng Dou, Sizhen Du & Guojie Song

Authors
  1. Peng Dou
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  2. Sizhen Du
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  3. Guojie Song
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Corresponding author

Correspondence to Guojie Song .

Editor information

Editors and Affiliations

  1. Peking University , Beijing, China

    Bin Cui

  2. The George Washington University, Washington, D.C., USA

    Nan Zhang

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

  4. University of Texas Rio Grande Valley, Edinburg, Texas, USA

    Xiang Lian

  5. Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

    Dexi Liu

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© 2016 Springer International Publishing Switzerland

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Dou, P., Du, S., Song, G. (2016). Inferring Diffusion Network on Incomplete Cascade Data. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_25

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  • DOI: https://doi.org/10.1007/978-3-319-39937-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39936-2

  • Online ISBN: 978-3-319-39937-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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