Two-Phase Approach to Link Prediction
Link prediction deals with predicting edges which are likely to occur in the future. The clustering coefficient of sparse networks is typically small. Link prediction performs poorly on networks having low clustering coefficient and it improves with increase in clustering coefficient. Motivated by this, we propose an approach, wherein, we add relevant non-existent edges to the sparse network to form an auxiliary network. In contrast to the classical link prediction algorithm, we use the auxiliary network for link prediction. This auxiliary network has higher clustering coefficient compared to the original network. We formally justify our approach in terms of Kullback-Leibler (KL) Divergence and Clustering Coefficient of the social network. Experiments on several benchmark datasets show an improvement of upto 15% by our approach compared to the standard approach.
KeywordsGraph Mining Local Similarity KL Divergence Clustering Coefficient Power-law degree distribution
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