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Technical standards support compatibility and interoperability among numerous entities existing in the telecommunications market. However, the university curricula rarely include standards analysis and rarely encourage students to create solutions that are based on the standards. This fact poses a hindrance in the adoption of new technologies due to the fact that recent graduates as young professionals will most certainly have difficulties in using new standards they may get in touch with on their first job. In our approach, we acknowledge the need for students to work with standards before they graduate. Research activities, connected with industrial projects in which application of new standardized technologies is in focus, are combined with student projects, enabling students to get their own hands-on experience in analyzing and applying telecommunications standards. As the result, students gain valuable experience for their future careers, an academic setting is used as a safe environment for testing and possibly even enhancing new standards, and the industry attains deeper knowledge about standards and technologies that could be used in their real-world deployments. [ABSTRACT FROM PUBLISHER]

The importance of machine learning for social network analysis is realized as an inevitable tool in forthcoming years. This is due to the unprecedented growth of social-related data, boosted by the proliferation of social media websites and the embedded heterogeneity and complexity. Alongside the machine learning derives much effort from psychologists to build computational model for solving tasks like recognition, prediction, planning and analysis even in uncertain situations. Therefore, it is significant to study the synergy of machine learning techniques in social network analysis, focus on practical applications, and open avenues for further research. In this paper, we have reviewed the theoretical aspects of social network analysis with a combination of machine learning-based techniques, its representation, tools and techniques used for analysis. Additionally, the source of data and its applications are also highlighted in this paper. [ABSTRACT FROM AUTHOR]