Dynamic human contact prediction based on naive Bayes algorithm in mobile social networks. (16th July 2019)

• Record Type: Journal Article
• Authors: Zeng, Feng; Yao, Lan; Wu, Baoling; Li, Wenjia; Meng, Lin
• Other Names: Wu Hao guestEditor.; Bie Rongfang guestEditor.; Pereira Charith guestEditor.; Rana Omer guestEditor.
• Is Part Of: Software, practice & experience. Volume 50:Number 11(2020)
• Page Start: 2031
• Page End: 2045
• Publication Date: 2019-07-16
• Subjects: clustering coefficient -- features extraction -- human contact prediction -- mobile social networks -- naive Bayes algorithm -- time interval; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2727
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Human contact prediction is a challenging task in mobile social networks. The existing prediction methods are based on the static network structure, and directly applying these static prediction methods to dynamic network prediction is bound to reduce the prediction accuracy. In this paper, we extract some important features to predict human contacts and propose a novel human contact prediction method based on naive Bayes algorithm, which is suitable for dynamic networks. The proposed method takes the ever‐changing structure of mobile social networks into account. First, the past time is partitioned into many periods with equal intervals, and each period has a feature matrix of all node pairs. Then, with the feature matrixes used for classifiers training based on naive Bayes algorithm, we can get a classifier for each time period. At last, the different weights are assigned to the classifiers according to their importance to contact prediction, and all classifiers are weighted combination into the final prediction classifier. The extensive experiments are conducted to verify the effectiveness and superiority of the proposed method, and the results show that the proposed method can improve the prediction accuracy and TP Rate to a large extent. Besides, we find that the size of time interval has a certain impact on the clustering coefficient of mobile social networks, which further affects the prediction accuracy.