BT-LPD: B+ Tree-Inspired Community-Based Link Prediction in Dynamic Social Networks

This paper presents a link prediction algorithm for dynamic social networks based on B+ trees. The authors pointed out the need for precise link prediction in social network analysis and asserted that current methods often produce inaccurate results as they are unable to account the social networks' dynamic nature. To circumvent this, a B+ tree-inspired community-based link prediction (BT-LPD) algorithm is proposed, which efficiently stores and retrieves the node information and associations and permits rapid queries of potential links. First, a community discovery technique for dynamic social networks inspired by B+ trees is described. The proposed method predicts missing links using community data and an approximation of influence flow. The performance is then evaluated using actual datasets and a number of cutting-edge methodologies. Findings show that the suggested BT-LPD technique outperforms the compared alternatives in terms of accuracy in dynamic social networks. It has important implications for social network analysis, recommender systems, and other applications that rely on accurate link prediction. The study introduces a novel and practical technique for link prediction in dynamic social networks.