Literature Review: Combining Machine Learning with Social Network Analysis Features

As the digital era witnesses unprecedented growth in social-related data, driven by the emergence of the Big Data concept and the ubiquity of social media. The symbiotic relationship between Social Network Analysis (SNA) and Machine Learning (ML) emerges as an indispensable tool for recognizing and decoding complex patterns. This paper provides an in-depth exploration of the state-of-the-art methodologies employed by researchers, addressing key research questions concerning the integration of SNA and ML. We outline a structured approach comprising nine sequential steps, facilitating comprehensive integration and optimization of data, features, and ML models. Challenges such as data preprocessing, framework selection, and feature transformation are discussed, alongside insights into balancing concerns and ML model predictions.