This paper addresses link prediction problem in directed networks by exploiting reciprocative nature of human relationships. It first proposes a null model to present evidence that reciprocal links influence the process of “triad formation”. Motivated by this, reciprocal links are exploited to enhance link prediction performance in three ways: (a) a reciprocity-aware link weighting technique is proposed, and existing weighted link prediction methods are applied over the resultant weighted network; (b) new link prediction methods are proposed, which exploit reciprocity; and (c) existing and proposed methods are combined toward supervised prediction to enhance the prediction performance further. All experiments are carried out on two real directed network datasets.