Self-Exciting Point Process Modeling of Conversation Event Sequences

  • Naoki MasudaEmail author
  • Taro Takaguchi
  • Nobuo Sato
  • Kazuo Yano
Part of the Understanding Complex Systems book series (UCS)


Self-exciting processes of Hawkes type have been used to model various phenomena including earthquakes, neural activities, and views of online videos. Studies of temporal networks have revealed that sequences of social interevent times for individuals are highly bursty. We examine some basic properties of event sequences generated by the Hawkes self-exciting process to show that it generates bursty interevent times for a wide parameter range. Then, we fit the model to the data of conversation sequences recorded in company offices in Japan. In this way, we can estimate relative magnitudes of the self excitement, its temporal decay, and the base event rate independent of the self excitation. These variables highly depend on individuals. We also point out that the Hawkes model has an important limitation that the correlation in the interevent times and the burstiness cannot be independently modulated.


Event Rate Direct Numerical Simulation Event Sequence Trigger Event Probability Generate Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



N. M. acknowledges the support provided through Grants-in-Aid for Scientific Research (No. 23681033, and Innovative Areas “Systems Molecular Ethology” (No. 20115009)) from MEXT, Japan. T. T. acknowledges the support provided through Grants-in-Aid for Scientific Research (No. 10J06281) from JSPS, Japan.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Naoki Masuda
    • 1
    Email author
  • Taro Takaguchi
    • 1
  • Nobuo Sato
    • 2
  • Kazuo Yano
    • 2
  1. 1.Department of Mathematical InformaticsThe University of TokyoTokyoJapan
  2. 2.Central Research Laboratory, Hitachi, Ltd.Kokubunji-shi, TokyoJapan

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