Leveraging Uncertainty Analysis of Data to Evaluate User Influence Algorithms of Social Networks

Identifying of highly influential users in social networks is critical in various practices, such as advertisement, information recommendation, and surveillance of public opinion. According to recent studies, different existing user influence algorithms generally produce different results. There are no effective metrics to evaluate the representation abilities and the performance of these algorithms for the same dataset. Therefore, the results of these algorithms cannot be accurately evaluated and their limits cannot be effectively observered. In this paper, we propose an uncertainty-based Kalman filter method for predicting user influence optimal results. Simultaneously, we develop a novel evaluation metric for improving maximum correntropy and normalized discounted cumulative gain (NDCG) criterion to measure the effectiveness of user influence and the level of uncertainty fluctuation intervals of these algorithms. Experimental results validate the effectiveness of the proposed algorithm and evaluation metrics for different datasets.