Analyzing the underlying social network is very important for the development of online applications. Owing to the increasingly growing size of these networks, parallel techniques play important roles in many network analysis tasks. In this paper, we explore the link sign prediction problem in largescale online social networks, and propose a parallel approach, called PLSP, to solve the problem. Specifically, we first extract a set of features that serve as a base for prediction. Experiments on several real datasets show that these features outperform those proposed by existing methods in predictive accuracy. Next, we present two speedup strategies, i.e. dataset division and feature selection, to shorten the training time. Experimental evaluations show that our parallel approach is much faster than the traditional non-parallel method and achieves higher predictive accuracy than other methods at the same time.
Zhou et al. REF presented a parallel technique called PLSP to improve the training speed of classifier based on social psychology theories.
A Parallel Approach to Link Sign Prediction in Large-Scale Online Social Networks
Comput. J.
Comput. J.
Computer Science