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Influence Propagation: Patterns, Model and a Case Study

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Advances in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8443))

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Abstract

When a free, catchy application shows up, how quickly will people notify their friends about it? Will the enthusiasm drop exponentially with time, or oscillate? What other patterns emerge?

Here we answer these questions using data from the Polly telephone-based application, a large influence network of 72,000 people, with about 173,000 interactions, spanning 500MB of log data and 200 GB of audio data.

We report surprising patterns, the most striking of which are: (a) the Fizzle pattern, i.e., excitement about Polly shows a power-law decay over time with exponent of -1.2; (b) the Rendezvous pattern, that obeys a power law (we explain Rendezvous in the text); (c) the Dispersion pattern, we find that the more a person uses Polly, the fewer friends he will use it with, but in a reciprocal fashion.

Finally, we also propose a generator of influence networks, which generate networks that mimic our discovered patterns

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Author information

Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Yibin Lin, Agha Ali Raza, Jay-Yoon Lee, Danai Koutra, Roni Rosenfeld & Christos Faloutsos

Authors
  1. Yibin Lin
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  2. Agha Ali Raza
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  3. Jay-Yoon Lee
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  4. Danai Koutra
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  5. Roni Rosenfeld
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  6. Christos Faloutsos
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Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan, R.O.C.

    Vincent S. Tseng  & Hung-Yu Kao  & 

  2. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Tu Bao Ho

  3. Nanjing University, China

    Zhi-Hua Zhou

  4. National Chengchi University, Taipei, Taiwan, R.O.C.

    Arbee L. P. Chen

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Lin, Y., Raza, A.A., Lee, JY., Koutra, D., Rosenfeld, R., Faloutsos, C. (2014). Influence Propagation: Patterns, Model and a Case Study. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8443. Springer, Cham. https://doi.org/10.1007/978-3-319-06608-0_32

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  • DOI: https://doi.org/10.1007/978-3-319-06608-0_32

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06607-3

  • Online ISBN: 978-3-319-06608-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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