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Learning the Hotness of Information Diffusions with Multi-dimensional Hawkes Processes

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Agents and Data Mining Interaction (ADMI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8316))

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Abstract

Modeling the information cascading process over networks has attracted a lot of research attention due to its wide applications in viral marketing, epidemiology and recommendation systems. In particular, information cascades can be useful for not only inferring the underlying structure of the network, but also providing insights on the properties of information itself. In this paper, we address the problem of jointly modeling the influence structure and the hotness of the information itself based on the temporal events describing the process of the information cascading. Specifically, we extend the multi-dimensional Hawkes process, which captures the mutual-excitation nature of information cascading, to further incorporate the hotness of the information being propagated. In the proposed method, the hotness of information and the network structure are modeled in a unified and principled manner, which enables them to reinforce each other and thus enhances the estimation of both. Experiments on both real and synthetic data show that our algorithm typically outperforms several existing methods and accurately estimates the hotness of information from the observed data.

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Notes

  1. 1.

    http://weibo.com/, which is the largest microblog service in China.

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Acknowledgments

This research was supported by National Natural Science Foundation of China (No. 61003107 and No. 61129001) and the High Technology Research and Development Program of China (No. 2012AA011702).

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Authors and Affiliations

  1. Shanghai Jiao Tong University, Dongchuan RD. 800, Shanghai, 200240, China

    Yi Wei & Ya Zhang

  2. Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Ke Zhou & Hongyuan Zha

Authors
  1. Yi Wei
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  2. Ke Zhou
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  3. Ya Zhang
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  4. Hongyuan Zha
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Corresponding authors

Correspondence to Yi Wei or Ke Zhou .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  2. Teesside University, Middlesbrough, United Kingdom

    Yifeng Zeng

  3. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Andreas L. Symeonidis

  4. St. Petersburg Institute for Informatics, St. Petersburg, Russia

    Vladimir Gorodetsky

  5. Department of Informatics, Technische Universität Clausthal, Clausthal-Zellerfeld, Germany

    Jörg P. Müller

  6. University of Illinois Chicago, Chicago, Illinois, USA

    Philip S. Yu

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Wei, Y., Zhou, K., Zhang, Y., Zha, H. (2014). Learning the Hotness of Information Diffusions with Multi-dimensional Hawkes Processes. In: Cao, L., Zeng, Y., Symeonidis, A., Gorodetsky, V., Müller, J., Yu, P. (eds) Agents and Data Mining Interaction. ADMI 2013. Lecture Notes in Computer Science(), vol 8316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55192-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-55192-5_8

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