Hidden naïve Bayesian model for social relation recommendation.

Relation recommendation based on common neighbors' property is a hot research branch of link prediction in social network analysis. This paper proposesd a new measure of relation recommendation by introducing a hidden naive Bayesians (HNB) classification model, which model the task by analyzing the dependency among properties and incorporates this idea to measure the influence and contribution among common neighbors. Then built a ranking model to learn the highest similarity associated with each candidate pair by maximizing the likelihood of relationship building and extended the model to CN, AA and RA similarity-based recommendation algorithms. Experimental evaluation by AUC on real social networks proved that the proposed model can achieve a better result than some baseline and LNB. Finally, it also discovered that attributes with different network topologies recommended precision linear effects. [ABSTRACT FROM AUTHOR]