¹College of Computer Science and Technology, Jilin University, Changchun, China.
²College of Software, Jilin University, Changchun, China.
³College of Computer Science and Technology, Jilin University, Changchun, China; Computer Science and Engineering, Arizona State University, Tempe, AZ, USA.

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Abstract—Trust relationships between user pairs play a vital role in making decisions for social network users. In reality, available explicit trust relations are often extremely sparse, therefore, inferring unknown trust relations attracts increasing attention in recent years. In this paper, a new approach originating from machine learning is proposed to predict trust relationships in social networks by exploring an improved k-nearest neighbor algorithm based on distance weight (WKNN). Firstly, we extract three critical attributes from users’ personal profiles and interactive information; then, an improved KNN algorithm named WKNN is proposed; finally, comparative analysis between them is performed by using real-world dataset from Epinions to evaluate their performance in trust prediction. Empirical evaluation demonstrates that the proposed framework (WKNN model) is feasible and effective in predicting trust relationships.

Index Terms—Trust relationships, social networks, WKNN, KNN.

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Cite: Qiuyue Zhao, Wanli Zuo, Zhongsheng Tian, Xin Wang, Ying Wang, "Predicting Trust Relationships in Social Networks Based on WKNN," Journal of Software vol. 10, no. 1, pp. 71-81, 2015.