Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

Abstract

SummaryIn recent years, social network analysis has received a lot of interest. A critical area of research in this field is link prediction. Link prediction is researched for other forms of social networks. Still, because social link networks (SLNs) change over time and depend on the discussed topics, this network has unique difficulties. Recent studies have focused on three main issues: extending link prediction to a dynamic environment, forecasting formation, and destroying network linkages that change over time. Although it is a challenging issue, deep learning (DL) techniques have been demonstrated to increase prediction accuracy significantly. This research proposes a novel approach to link correlation for social networks based on DL architectures in feature vector prediction and classification. Here the input data has been processed for smoothening and normalization with noise removal. Then, the feature vector was predicted using a dynamically structured convolutional radial basis neural network for this data. The expected feature vector has been classified using a stochastic gradient‐based graph neural network. The experimental analysis is carried out for various social network data in terms of accuracy of 98%, precision of 85%, recall of 86%, F‐1 score of 75%, AUC of 72%, and RMSE of 76%.