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Learning Diffusion Probability based on Node Attributes in Social Networks
| Citations: | 7 - 1 self |
BibTeX
@MISC{Saito_learningdiffusion,
author = {Kazumi Saito and Kouzou Ohara and Yuki Yamagishi and Masahiro Kimura},
title = {Learning Diffusion Probability based on Node Attributes in Social Networks},
year = {}
}
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Abstract
Abstract. Information diffusion over a social network is analyzed by model-ing the successive interactions of neighboring nodes as probabilistic processes of state changes. We address the problem of estimating parameters (diffusion probability and time-delay parameter) of the probabilistic model as a function of the node attributes from the observed diffusion data by formulating it as the maximum likelihood problem. We show that the parameters are obtained by an iterative updating algorithm which is efficient and is guaranteed to converge. We tested the performance of the learning algorithm on three real world networks assuming the attribute dependency, and confirmed that the dependency can be correctly learned. We further show that the influence degree of each node based on the link-dependent diffusion probabilities is substantially different from that obtained assuming a uniform diffusion probability which is approximated by the average of the true link-dependent diffusion probabilities. 1
Keyphrases
diffusion probability social network node attribute real world network information diffusion successive interaction observed diffusion data state change true link-dependent diffusion probability probabilistic model link-dependent diffusion probability influence degree uniform diffusion probability probabilistic process time-delay parameter attribute dependency maximum likelihood problem iterative updating algorithm
