Various approaches of knowledge transfer in academic social network

Abstract

Real diffusion networks area unit advanced and dynamic, since underlying social structures don't seem to be solely comprehensive on the far side one homogenised system however conjointly often ever-changing with the context of diffusion. Thus, finding out topic-related diffusion across multiple social systems is vital for an improved understanding of such realistic things. consequently, this paper focuses on uncovering topic-related diffusion dynamics across heterogeneous social networks in each model-driven and model-free ways. We discover that the 2 approaches offer similar results however with completely different views that in conjunction will facilitate higher make a case for diffusion than either approach alone. They conjointly recommend different choices as either or each of the approaches are often used acceptable to the important things of various application domains. We expect that our planned approaches offer ways in which to quantify and under-stand crosspopulation diffusion trends at a macro level. Also, they'll be applied to a good vary of analysis areas like science, marketing, and even neurobiology, for estimating dynamic influences among target regions or systems.