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2023 ◽  
Vol 55 (1) ◽  
pp. 1-51
Huacheng Li ◽  
Chunhe Xia ◽  
Tianbo Wang ◽  
Sheng Wen ◽  
Chao Chen ◽  

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 (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 (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.

2022 ◽  
Vol 113 ◽  
pp. 385-393
Hisam Samae ◽  
Surajit Tekasakul ◽  
Perapong Tekasakul ◽  
Worradorn Phairuang ◽  
Masami Furuuchi ◽  

2022 ◽  
Vol 115 ◽  
pp. 265-276
Min Xu ◽  
Zirui Liu ◽  
Bo Hu ◽  
Guangxuan Yan ◽  
Jianan Zou ◽  

2022 ◽  
Vol 112 ◽  
pp. 25-37
Peng Xu ◽  
Yuan Yang ◽  
Junke Zhang ◽  
Wenkang Gao ◽  
Zirui Liu ◽  

Maimuna O. Abdulraheem ◽  
Jamiu Adetayo Adeniran ◽  
Hafsat Abolore Ameen ◽  
Emmanuel Toluwalope Odediran ◽  
Muhammad-Najeeb O. Yusuf ◽  

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