Analysis and construction by convolution neural network of link prediction model on social network

Abstract

Estimating similarity using multiple similarity measures or machine learning prediction models is a popular solution to the link prediction problem. The Relation Pattern Deep Learning Classification (RPDLC) technique is proposed in this study, and it is based on multiple neighbor-based similarity metrics and convolution neural networks. The RPDLC first calculates the characteristics for a pair of nodes using neighbor-based metrics and impact nodes. Second, the RPDLC creates a heat map using node characteristics to assess the similarity of the nodes’ connection patterns. Third, the RPDLC uses convolution neural network architecture to build a prediction model for missing relationship prediction. On three separate social network datasets, this method is compared to other state-of-the-art algorithms. On all three datasets, the suggested method achieves the greatest AUC, hovering around 99 percent. The use of convolution neural networks and features via relational patterns to create a prediction model are the paper’s primary contributions.