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Link prediction is an important research direction in the field of Social Network Analysis. The significance of this research area is crucial especially in the fields of network evolution analysis and recommender system in online social networks as well as e-commerce sites. This paper aims at predicting the hidden links that are likely to occur in near future. The possibility of formation of links is based on the similarity score between pair of nodes that are not yet connected in the social network. The similarity score, which we call link prediction score has been evaluated in Map-Reduce programming model. The proposed similarity score is based on both the structural information around the nodes and the degree of influence for neighboring nodes. The proposed algorithm is scalable in nature and performs quite well for large scale complex networks having good number of nodes and edges based on large pool of data or often termed as big-data. The efficiency and effectiveness of the algorithms are extensively tested and compared against traditional link prediction algorithms using three real world social network datasets. Keywords—Link Prediction; Preferential Attachment; Sim Rank; Jaccard Coefficient; Kartz Measure

Map-Reduce based Link Prediction for Large Scale Social Network