Special Session: Advanced Computational Big-Data and Social Network

Network science can be seen as a specialization of data science that focuses on network data, or alternatively, as a particular method in complexity research. Typically, the network perspective reveals patterns and emerging phenomena that are not visible when the mere individual objects in the data are studied. Many real-world systems can be modeled as a network, in which, social networks are of great important because of its popularity, computational challenges due to their large scale.

This Special Session (SS) provides an interdisciplinary forum to bring together researchers and practitioners from all fields of big data and social networks, such as billion-scale network computing, social network/media analysis, mining, security and privacy, and deep learning. The session solicits theoretical, methodological, empirical, and experimental research reporting original and unpublished results on computational big data and social networks. Topics of interest include, but are not limited to:

  • Real-world Complex Networks Analysis
  • Trends and Pattern Analysis in Social Networks
  • Representation Learning on Networks
  • Big Data Network Analysis
  • Mathematical Modeling and Analysis of Real-world Social Platforms
  • Network Structure Analysis and Dynamics Optimization
  • Data Network Design and Architecture
  • Information Diffusion Models and Techniques
  • Security and Privacy in Data Networks
  • Efficient Algorithms for Large-scale Data Networks Computing
  • Reputation and Trust in Social Media
  • Social Influence, Recommendation, and Media
  • Applications of Complex Data Network Analysis
  • Natural Language Understanding for Social Media
  • E-commerce and Social Media Marketing
  • Deep Learning on Graphs and its Application
  • Stock Market Prediction and Stock Recommendation with Social Media Data
  • Anomaly Detection, Security, and Privacy in Big Data Networks
  • Analysis of signed and attributed real-world networks
  • Multidimensional graph analysis
  • Algorithmic fairness in social network analysis and graph mining.


  • Thanh-Trung Nguyen, Ho Chi Minh City University of Foreign Languages – Information Technology, Vietnam (trungnt@huflit.edu.vn)
  • Ha Manh Tran, Ho Chi Minh City University of Foreign Languages – Information Technology, Vietnam (hatm@huflit.edu.vn)
  • Quang Nguyen, Duy Tan University, Vietnam (email: nguyenquang29@duytan.edu.vn)


  • All papers must be original and not simultaneously submitted to another journal or conference. Authors are invited to submit papers of up to 6 pages, written in English, in PDF format, and compliant with the IEEE standard.
  • Papers must be limited to six pages, including text, references, tables and figures, and should be submitted online on EDAS system
  • At least one author of each accepted paper is required to pay the registration fee and to present the paper either in-person at the conference or online. Accepted papers presented at the conference will be submitted for inclusion in the IEEE Xplore.

Important Dates

  • Manuscript submission: October 30, 2022
  • Notification of acceptance: November 20, 2022
  • Camera-ready submission: November 30, 2022


All accepted papers will be published in the RIVF 2022 Conference Proceedings. Presented papers will be included in the IEEE Xplore.

More information can be found at https://rivf2022.huflit.edu.vn/ or contact special session co-chairs.