DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property
Scientific Reports, 2024Cited by 5
Publications
Inferring Dynamic Diffusion Networks in Online Media
ACM Transactions on Knowledge Discovery from Data, 14 June 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.
In short
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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