Call for Papers: Special Issue on Big Data Analytics in Complex Social Information Networks

IEEE TBD seeks submissions for this upcoming special issue.
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Submissions Due: 30 July 2024

Important Dates

  • Submission Deadline:  30 July 2024
  • First-Round Review Notification: 30 October 2024
  • Final Decision Notification: 30 Dec 2024

Publication: Early 2025

With the explosion of social media platforms and the increasing interconnectedness of society, social information networks have become a rich source of data for analysis. The analysis of complex social information networks using big data analytics presents unique challenges due to the vast amounts of data generated and the complexity of the networks themselves. Big data analytics can provide solutions for managing, analyzing, and utilizing the vast amount of data generated by social information networks. These technologies can analyze vast amounts of data from multiple sources, including social media, news outlets, and other relevant sources, and extract valuable insights that can inform risk strategies (Pal A et al. 2023; Wang et al.2018;Lyu et al 2020). 

Although big data analytics plays an important role in complex social information networks, this field faces many challenges and difficulties One of the challenges is the complexity of the data, which is often large-scale, high-dimensional, and unstructured in social information networks, requiring the use of specialized data analysis methods and tools for processing (Ruan et al. 2018). In addition, with the popularity of social media, the data in social information networks is constantly growing, making data storage, management, processing, and analysis a highly challenging task. The data may be unstructured, coming from different formats and sources, and may require significant cleaning and pre-processing before it can be analyzed effectively. Privacy and security concerns are also significant challenges when working with big data. Collecting and storing large amounts of personal information can raise privacy concerns, and the risk of data breaches and cyber attacks must be carefully managed. There is also a significant challenge in ensuring that the insights generated from these technologies are translated into effective decision-making. Organizations must ensure that the insights are effectively communicated and understood by decision-makers and that they are incorporated into decision-making processes effectively.

This special issue aims to bring together researchers and practitioners to explore the theoretical, empirical, and practical aspects of Big Data Analytics in Complex Social Information Network. We seek original research articles that offer insights and solutions to the challenges and opportunities.

We welcome submissions that address a range of topics, including but not limited to :

  • Big data-driven optimization models
  • Big data, complex networks and risk analysis
  • Novel big data analytics methods for social information network analysis
  • Machine learning and deep learning approaches for social network analysis
  • Visualization techniques for complex social networks
  • Social network modeling and prediction
  • Community detection and identification in social networks
  • Influence and opinion propagation analysis in social information networks
  • Privacy and security issues in big data analytics on social information networks

Submissions will undergo a rigorous double-blind review process, and accepted papers will be published in a special issue of a leading academic journal.

Submission Guidelines

For author information and guidelines on submission criteria, please visit the TBD’s Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.


Pal, Amitangshu, et al. “Social Media Driven Big Data Analysis for Disaster Situation Awareness: A Tutorial.” IEEE Transactions on Big Data (2022).
Wang, Pengyang, et al. “Learning urban community structures: A collective embedding perspective with periodic spatial-temporal mobility graphs.” ACM Transactions on Intelligent Systems and Technology (TIST) 9.6 (2018): 1-28.
Lyu, Hanjia, et al. “Sense and sensibility: Characterizing social media users regarding the use of controversial terms for covid-19.” IEEE Transactions on Big Data 7.6 (2020): 952-960.


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