BibTeX
@MISC{Doo_extractingtop-k,
author = {Myungcheol Doo and Ling Liu},
title = {Extracting Top-k Most Influential Nodes by Activity Analysis},
year = {}
}
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Abstract
Can we statistically compute social influence and under-stand quantitatively to what extent people are likely to be influenced by the opinion or the decision of their friends, friends of friends, or acquaintances? An in-depth under-standing of such social influence and the diffusion process of such social influence will help us better address the ques-tion of to what extent the ’word of mouth ’ effects will take hold on social networks. Most of the existing social in-fluence models to define the influence diffusion are solely based on topological connectivity of social network nodes. In this paper, we presented an activity-base social influence model. Our experimental results show that activity-based social influence is more effective in understanding the viral marketing effects on social networks. 1
Keyphrases
top-k influential node activity analysis social influence social network viral marketing effect diffusion process topological connectivity in-depth under-standing activity-based social influence social network node mouth effect influence diffusion experimental result social in-fluence model extent people activity-base social influence model
