Advanced Data Mining Methods for Social Computing

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:

• Network representation learning
• Streaming social data processing
• Heterogeneous social network mining
• Behavior analysis of social networks
• Social multimedia and image processing
• Social text analysis
• Deep learning in social computing
• Behavior analysis on social networks
• Pattern recognition of behaviors
• Human sentiment mining and analysis
• Individual interest modeling
• Personalized recommender systems
• Knowledge graph and its applications
• Essential mechanism of information diffusion and control
• Modeling the formation and phase transition of collective phenomena
• Trend prediction of information propagation
• Modeling and analysis of interpersonal interactions
• Multimedia data analysis

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

Guest Editors:

1. Shirui Pan, Monash University, Australia
2. Jia Wu, Macquarie University, Australia
3. Huaiyu Wan, Beijing Jiaotong University, China
4. Huizhi Liang, University of Reading, United Kingdom
5. Haishuai Wang, Fairfield University, USA
6. Huawei Shen, Chinese Academy of Sciences, China

Relevant IEEE Access Special Sections:

1. Applications of Big Data in Social Sciences
2. AI-Driven Big Data Processing: Theory, Methodology, and Applications
3. Privacy Preservation for Large-Scale User Data in Social Networks

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: zhaoyq@nwpu.edu.cn.

.