Recent Trends and Applications of Data Science in Social Network

Dear Colleagues,

(1) Data science techniques and algorithms are currently widely applied to analyze data about social networks. Such applications are very important in many business and social sciences areas. For example, data science and analytics allow media platforms to classify users on the basis of their interests and behaviors. In particular, the ever-increasing adoption of AI techniques in the field of data science can enable very quick data analytic sessions and better results.

(2) In this context, this Special Issue is mainly aimed at collecting a wide range of contributions about novel algorithms and techniques, as well as applications, in the field of data science, with specific reference to social networks. Surveys about the latest views proposed in the literature are welcome, as well as original contributions from practitioners about techniques, protocols, algorithms, and architectures in the field of data science and analytics in social networks and social sciences. Proposals about AI-driven applications for data science are welcome, as well as those regarding the discussion of aspects related to data science and analytics in social networks; for example, security, privacy, and trust in the specific context of data collection and analytics. Proposals of novel architectures and standards for data science with particular focus on social networks are welcome.

(3) Suggest themes.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

• Architecture and models for data science and analytics in social networks.
• Protocols and standards for data science and analytics in social networks.
• AI-based agents and applications for data science in social networks and social sciences.
• Fog/Edge and cloud AI-based systems for data science and analytics.
• Trust and recommendations systems for data science in social networks.
• Models, protocols, and algorithms for energy-aware analytics techniques.
• Data mining and applied machine learning in social networks.
• Trust and responsible AI applications for data science in social networks.
• Data-driven application for social and professional networks.

We look forward to receiving your contributions. 

Dr. Fabrizio Messina
Guest Editor

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• data science
• data analytics
• social sciences
• social networks
• data mining
• AI-based data analytics
• agent-based data-driven applications
• data-driven architecture
• data science protocols