Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

TL;DR

This survey comprehensively reviews methodologies and techniques for modeling information diffusion in social networks, proposing a unified framework, taxonomy, and comparative analysis of models, and discussing future research directions.

Contribution

It introduces a unified diffusion concept, a hybrid taxonomy, and provides a comparative study of elementary models, advancing understanding of diffusion modeling in SNS.

Findings

Proposed a unified diffusion framework with three components.

Developed a hybrid taxonomy based on granularity and techniques.

Compared elementary diffusion models on assumptions and methods.

Abstract

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (\textit{e.g.}, public opinion monitoring, rumor source identification, and viral marketing.) Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (\textit{i.e.,} granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.