(4) Our model is based on non-autonomous partial differential equations which are proposed to characterize temporal and spatial patterns of information diffusion over online social networks. .
Our model is based on non-autonomous partial differential equations which are proposed to characterize temporal and spatial patterns of information diffusion over online social networks.
A number of concerns in modeling information diffusion in online social networks have been put forward on the basis of PDE-based models.
As is well known, studying information diffusion in online social networks by PDE-based models is very difficult, and this presents a new opportunity and challenge for mathematicians.
As is well known, partial differential equations (PDEs) can describe temporal and spatial patterns of information diffusion over online social networks; however, until now, results for understanding information propagation of social networks over both temporal and spatial dimensions are few.
The mechanism of information diffusion on online social networks including characterizing user behavior, characterizing social cascades in Flickr, network level footprints of Facebook, applications etc. [6 15] has been studied by many authors.
The Structural Virality of Online Diffusion, Sharad Goel, Ashton Anderson, Jake Hofman, Duncan J. Watts, Management Science, 2015.
It turns out the paper, "The Structure of Online Diffusion Networks," by Goel, Duncan J. Watts and Daniel G. Goldstein (all of Yahoo Researchh at the time, now at Microsoft Research) was first presented last January, and is publicly available.
However, due to the inherent challenges resulting from the dynamic diffusion mechanism in online social networks, such as modeling of activation thresholds and influence probability, differentiating between influence and adoption, and incorporating review content, traditional diffusion models are often not adequate enough to predict product adoption accurately.
This article has provided hints about the characterization of the organization of political parties according to their online diffusion networks.
