Research Topic

Data-Driven Mathematical and Statistical Models of Online Social Networks

About this Research Topic

With the development of modern technology, various platforms emerge and offer great convenience to our lives. Though scholars devote their efforts to the investigation of social networks, there is still an urgent need for more understanding of the modeling, analysis and effective control methods which can offer to the information spreading dynamics prediction. The rapid development of Web technology in the Internet era drives the social network to become the prominent way of spreading online information and the orderly development of social networks plays an important role in affecting state safety or public security. This provides efficient tools for the spreading of information compared with the traditional offline approaches. The spreading of various information on social networks is significant to different areas, such as, public health, society, industry, economics, and technology.

We are anticipated to perform analysis of multi-layered online social networks, including network modeling, network construction, network analysis, etc. Through the analysis of multi-layered networks from data, we are supposed to provide helpful insights into the study of online social networks from multi-plant forms, such as network construction through artificial intelligence approaches, source identification, influence maximization and spreading dynamics prediction. Through analysis of the interactions between offline and online networks, we are supposed to get more insights regarding the development of modern online social networks.

This Research Topic aims to extend the angles and collect articles that focus on data-driven mathematical or statistical models of online social networks, also analysis of interactions between online and offline networks. Furthermore, we also want to understand the dynamic prediction on multi-layered networks and provide efficient suggestions for the spreading of desired information. The editors and reviewers of this Research Topic will guarantee a fast, but fair, peer-to-peer review procedure, to provide to society a reliable injection of scientific insights. High-quality Original Research and Review articles in this field are all welcome for submission to this Research Topic. Research interests include but are not limited to the following areas:

1. Modeling of multiplex networks
2. Nonlinear dynamics investigation of social networks
3. Network construction from data
4. Online social network analysis
5. Machine learning and big data analysis of social networks
6. Source identification in online networks
7. Community detection in multi-layered and dynamic networks
8. Attack and defense of social networks
9. Application to economics, engineering and other aspects related to social science


Keywords: network dynamics, multi-layered networks, network construction, community detection, social networks


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

With the development of modern technology, various platforms emerge and offer great convenience to our lives. Though scholars devote their efforts to the investigation of social networks, there is still an urgent need for more understanding of the modeling, analysis and effective control methods which can offer to the information spreading dynamics prediction. The rapid development of Web technology in the Internet era drives the social network to become the prominent way of spreading online information and the orderly development of social networks plays an important role in affecting state safety or public security. This provides efficient tools for the spreading of information compared with the traditional offline approaches. The spreading of various information on social networks is significant to different areas, such as, public health, society, industry, economics, and technology.

We are anticipated to perform analysis of multi-layered online social networks, including network modeling, network construction, network analysis, etc. Through the analysis of multi-layered networks from data, we are supposed to provide helpful insights into the study of online social networks from multi-plant forms, such as network construction through artificial intelligence approaches, source identification, influence maximization and spreading dynamics prediction. Through analysis of the interactions between offline and online networks, we are supposed to get more insights regarding the development of modern online social networks.

This Research Topic aims to extend the angles and collect articles that focus on data-driven mathematical or statistical models of online social networks, also analysis of interactions between online and offline networks. Furthermore, we also want to understand the dynamic prediction on multi-layered networks and provide efficient suggestions for the spreading of desired information. The editors and reviewers of this Research Topic will guarantee a fast, but fair, peer-to-peer review procedure, to provide to society a reliable injection of scientific insights. High-quality Original Research and Review articles in this field are all welcome for submission to this Research Topic. Research interests include but are not limited to the following areas:

1. Modeling of multiplex networks
2. Nonlinear dynamics investigation of social networks
3. Network construction from data
4. Online social network analysis
5. Machine learning and big data analysis of social networks
6. Source identification in online networks
7. Community detection in multi-layered and dynamic networks
8. Attack and defense of social networks
9. Application to economics, engineering and other aspects related to social science


Keywords: network dynamics, multi-layered networks, network construction, community detection, social networks


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

30 June 2021 Abstract
01 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 June 2021 Abstract
01 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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