Mapping Social Connections: A Graph Mining and Ensemble learning Approach to Link Prediction
Devang Gangal, Aniket Kadam, Neeraj Salunke, Amit Aylani · 2023
This research presents an innovative approach for predicting linkages in social network graphs using graph mining techniques. Link prediction is a key issue in network research and has a wide range of real-world uses, such as customised suggestions and targeted marketing. The most likely new interactions between social media users in the near future can be predicted using a social network’s graph. Our suggested approach makes use of the power of graph mining techniques to search for information on the system architecture and node attributes. This paper provides a variety of feature engineering methodologies that are used to convert this data into useful input. These traits are then fed into the machine learning model that is used to forecast links. We test the effectiveness of our method using real social network datasets. This study can further optimize our understanding of social network dynamics and aid in decision-making in a variety of contexts.