Machine Learning for Social Network Analysis: A Systematic Literature Review
Year: 2012
Type: preprint
Abstract: 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.
Source: SSRN Electronic Journal
Authors: Satchidananda Dehuri, Sagar S. De, Gi-Nam Wang
Institutions: Ajou University, S.N. Bose National Centre for Basic Sciences
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W156844685