The IUP Journal of Information Technology, Vol. VIII, No. 4, pp. 30-51, December 2012
Posted: 10 Dec 2012
Date Written: December 10, 2012
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.
Keywords: social networks, machine learning, clustering
Suggested Citation: Suggested Citation
Dehuri, Satchidananda and De, Sagar S. and Wang, Gi-Nam, Machine Learning for Social Network Analysis: A Systematic Literature Review (December 10, 2012). The IUP Journal of Information Technology, Vol. VIII, No. 4, pp. 30-51, December 2012. Available at SSRN: https://ssrn.com/abstract=2187186