Generative Models of Information Diffusion with Asynchronous Timedelay
Kazumi Saito (University of Shizuoka), Masahiro Kimura (Ryukoku
University), Kouzou Ohara (Aoyama Gakuin University), and Hiroshi
Motoda (Osaka University);
JMLR W&P 13:193-208, 2010.
We address the problem of formalizing an information diffusion process
on a social network as a generative model in the machine learning
framework so that we can learn model parameters from the observation.
Time delay plays an important role in formulating the likelihood
function as well as for the analyses of information diffusion. We identified
that there are two different types of time delay: link delay and
node delay. The former corresponds to the delay associated with information
propagation, and the latter corresponds to the delay due to
human action. We further identified that there are two distinctions
of the way the activation from the multiple parents is updated: nonoverride
and override. The former sticks to the initial activation and
the latter can decide to update the time to activate multiple times. We
formulated the likelihood function of the well known diffusion models:
independent cascade and linear threshold, both enhanced with asynchronous
time delay distinguishing the difference in two types of delay
and two types of update scheme. Simulation using four real world networks
reveals that there are differences in the spread of information
diffusion and they strongly depend on the choice of the parameter
values and the denseness of the network.