Influence of edge weight on node proximity based link prediction methods: An empirical analysis

Tie weight plays an important role in maintaining cohesiveness of social networks. However, influence of the tie weight on link prediction has not been clearly understood. In few of the previous studies, conflicting observations have been reported. In this paper, we revisit the study of the effect of tie weight on link prediction. Previous studies have focused on additive weighting model. However, the additive model is not suitable for all node proximity based prediction methods. For understanding the effect of weighting models on different prediction methods, we propose two new weighting models namely, min-flow and multiplicative. The effect of all three weighting models on node proximity based prediction methods over ten datasets of different characteristics is thoroughly investigated. From several experiments, we observe that the response of different weighting models varies, subject to prediction methods and datasets. Empirically, we further show that with the right choice of a weighting model, weighted versions may perform better than their unweighted counterparts.We further extend the study to show that proper tuning of the weighting function also influences the prediction performance. We also present an analysis based on the properties of the underlying graph to justify our result. Finally, we perform an analysis of the weak tie theory, and observe that unweighted models are suitable for inter-community link prediction, and weighted models are suitable for intra-community link prediction.