A differential machine learning approach for trust prediction in signed social networks

Understanding the dynamic nature of group formation and evolution in social networks is seen as a significant step to better describe how individuals’ trust nodes are gathered and form communities. There has been considerable growth in interest in the modeling and evaluation of signed social networks (SSNs) and extracting their essential properties to improve their trustiness. In order to improve the trustiness of these networks, we propose a differential machine learning-based approach to predict trust values in SSNs, called diffTrbML. Differential machine learning considers inputs, labels, and differentials of labels to inputs to enhance training performance. By training on collected real-world datasets customized by trust feature vectors for SSNs, a trust value predictor is established to calculate the node trust value. We also evaluate prediction results using common machine learning measures including precision, recall, F1 score, accuracy, and RMSE on two real-world SSNs for nodes based on trust values. Lastly, diffTrbML resulted in significantly greater levels of learning measures when customized features for SSNs were investigated. The experiments in large social networks show that the proposed trust prediction method can provide a high-performance learning approach.