Submission Deadline: 31 December 2019
IEEE Access invites manuscript submissions in the area of Advanced Data Mining Methods for Social Computing.
Social networks have become an important way for individuals to communicate with each other. Various kinds of social networks develop explosively, such as online social networks, scientific cooperation networks, athlete networks, airport passage networks, etc. Social networks have increasingly demonstrated their strength due to their large number of participants and real-time information dissemination capability. Social computing has become a promising research area and attracts much attention. Analyzing and mining human behavior, topological structure and information diffusion in social networks can help to understand the essential mechanism of macroscopic phenomena, discover potential public interest, and provide early warnings of collective emergencies.
In the past, to study the characteristics of social networks, questionnaires were designed, and volunteers in the network were invited to complete questionnaires. However, the amount of data collected from questionnaires was not enough to understand the whole perspective and essential mechanism of social events. With the development of mobile sensing, computer networks and artificial intelligence in recent years, it is possible to collect an abundance of data from various social multimedia. Big data in social networks also bring challenges in how to process social data and investigate human behavior. In addition, there are new and complex features in social networks, such as heterogeneous human properties, dynamic network structures and random interpersonal interactions. Therefore, advanced multidisciplinary data collection and data mining methods should be proposed for social computing and developed to study social networks.
This Special Section in IEEE Access welcomes contributions in the quickly growing field of social computing. We encourage articles with multidisciplinary methods for social data mining. The related disciplines include machine learning, information theory, mathematics, computational and statistical physics.
The topics of interest include, but are not limited to:
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Associate Editor: Yongqiang Zhao, Northwestern Polytechnical University, China
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IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
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