Inferring Dynamic Diffusion Networks in Online Media

TitleInferring Dynamic Diffusion Networks in Online Media
Publication TypeJournal Article
Year of Publication2016
AuthorsRabiee, H. R., and M. Ramezani
JournalACM Transactions on Knowledge Discovery from Data
Start Page44-1
Date Published06/2016
KeywordsDynamic Network, Hidden Markov Model, Information Diffusion, Online Media
AbstractOnline media play an important role in information societies by providing a convenient infrastructure for different processes. Information diffusion that is a fundamental process taking place on social and information networks has been investigated in many studies. Research on information diffusion in these networks faces two main challenges: 1) In most cases diffusion takes place on an underlying network which is latent and its structure is unknown. 2) This latent network is not fixed and changes over time. In this paper, we investigate the diffusion network extraction problem when the underlying network is dynamic and latent. We model the diffusion behavior (existence probability) of each edge as a stochastic process and utilize the Hidden Markov Model to discover the most probable diffusion links according to the current observation of the diffusion process, which is the infection time of nodes and the past diffusion behavior of links. We evaluate the performance of our Dynamic Diffusion Network Extraction (DDNE) method, on both synthetic and real datasets. Experimental results show that the performance of the proposed method is independent of the cascade transmission model and outperforms the state of art method in terms of F-measure.
DOI<a href=";amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;