Enhancing link prediction through node embedding and ensemble learning

Abstract

Social networks, characterized by their dynamic and continually evolving nature, present challenges for effective link prediction (LP) due to the constant addition of nodes and connections. In response to this, we propose a novel approach to LP in social networks through Node Embedding and Ensemble Learning (LP-NEEL). Our method constructs a transition matrix from the network’s adjacency matrix and computes similarity measures between node pairs. Utilizing node2vec embedding, we extract features from nodes and generate edge embeddings by computing the inner product of node embeddings for each edge. This process yields a well-labeled dataset suitable for LP tasks. To mitigate overfitting, we balance the dataset by ensuring an equal number of negative and positive samples edge samples during both the testing and training phases. Leveraging this balanced dataset, we employ the XGBoost machine learning algorithm for final link prediction. Extensive experimentation across six social network datasets validates the efficacy of our approach, demonstrating improved predictive performance compared to existing methods.