Inferring Dynamic Diffusion Networks in Online Media
Maryam Tahani, Ali Mohammad Afshin Hemmatyar, Hamid R. Rabiee, Maryam Ramezani
ACM Transactions on Knowledge Discovery from DataJune 14, 2016
Abstract
Diffusion networks in online media are not static: the links over which information travels appear, disappear and change strength over time. This work infers the dynamic structure of such networks from observed propagation, rather than assuming a single fixed graph behind all cascades.
Most network inference methods assume one graph generated every cascade. Online media do not work that way: the edges that carry information change over weeks and months.
This paper infers that changing structure from the propagation data itself.
Cite this paper
@article{tahani2016inferring,
title = {{Inferring Dynamic Diffusion Networks in Online Media}},
author = {Maryam Tahani and Ali Mohammad Afshin Hemmatyar and Hamid R. Rabiee and Maryam Ramezani},
journal = {ACM Transactions on Knowledge Discovery from Data},
year = {2016}
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