OWA operator based link prediction ensemble for social network

• This paper firstly studied the link prediction ensemble for local information based algorithms.
• The integration of individual algorithms is finished via OWA operator.
• Experimental results reveal the better performances of our proposed link prediction ensemble algorithm.

The objective of link prediction for social network is to estimate the likelihood that a link exists between two nodes. Although there are many local information-based algorithms which have been proposed to handle this essential problem in the social network analysis, the empirical observations show that the stability of local information-based algorithm is usually very low, i.e., the variabilities of local information-based algorithms are high. Thus, motivated by obtaining a stable link predictor with low variance, this paper proposes a kind of ordered weighted averaging (OWA) operator based link prediction ensemble algorithm (LPEOWA) for social network by assigning the aggregation weights for nine local information-based link prediction algorithms with three different OWA operators. The finally experimental results on benchmark social network datasets show that LPEOWA obtains a more stable prediction performance and considerably improves the prediction accuracy which is measured by the area under the receiver operating characteristic curve (AUC) in comparison with nine individual prediction algorithms.