The social networks, such as Twitter, Facebook and WeChat, organize a massive amount of data (volume). The data is fast increased (velocity). It contains rich information and knowledge about people's daily lives and penetrates to many domains, such as politics, business, marketing, social interaction, online education, knowledge sharing (variety). Big Data techniques for online social network data lead to promising solutions for challenges in social network analysis. Topics of interest include but are not limited to:
Authors who are interested in the above topics can submit their unpublished work to Social Networking Mining in BigData in CSoNet2016 via the Springer Online Conference System at https://ocs.springer.com/ocs/home/CSoNet2016. A clear indication of the motivation and comparison with prior related work should be presented. Simultaneous submissions to a journal or another conference with refereed proceedings are not allowed.
Submitted papers should be prepared in LNCS style and should not exceed 12 pages. Each paper is to be submitted electronically as a single PDF file through the Springer Online Conference System at https://ocs.springer.com/ocs/home/CSoNet2016
Note that proofs omitted due to space constraints must be placed in an appendix to be read by the program committee members at their discretion. All accepted papers must be presented by one of the authors who must register for the conference and pay the fee.
The accepted papers will be included in the proceedings of the main conference, CSoNet 2016, which will published by Springer in series Lecture Notes in Computer Science (LNCS) and will be distributed at the conference. Selected papers can be invited to publish in special issues of Journal of Combinatorial Optimization (ISI) and Computational Social Networks (Springer).
August 2-4, 2016 - Ho Chi Minh City, Viet Nam