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• [P345] A Deterministic Model for History Sensitive Cascade in Difussion Networks
  Author(s): Yu Zhang
  
  

  Abstract
  This paper studies information diffusion in networks. Traditional models are all history insensitive, i.e. only giving activated nodes a one-time chance to activate each of its neighboring nodes with some probability. But history dependent interactions between people are often observed in real world. This paper propose a new model called the History Sensitive Cascade Model (HSCM) that allows activated nodes to receive more than a one-time chance to activate their neighbors. HSCM is a deterministic model to decide the probability of activity for any arbitrary node at any arbitrary time step. In particular, we provide 1) a polynomial algorithm for calculating this probability in tree structure graphs, and 2) a Markov model for calculating the probability in general graphs. Finally, we perform an empirical study on HSCM under different network settings. These simulations have showed its power to observe and explain the emergent phenomena in the macro level when changing parameters in the micro level.

  
   

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