Role of Weak Ties in Link Prediction of Complex Networks

TL;DR

This paper investigates the role of weak ties in link prediction within complex networks, revealing that emphasizing weak ties can significantly improve prediction accuracy, despite weighted indices performing worse.

Contribution

It introduces an extensive experimental analysis showing the importance of weak ties in link prediction and highlights the counterintuitive finding that weighted indices underperform.

Findings

Weighted indices perform worse than unweighted ones.

Weak ties significantly enhance link prediction accuracy.

Emphasizing weak ties improves prediction performance.

Abstract

Plenty of algorithms for link prediction have been proposed and were applied to various real networks. Among these works, the weights of links are rarely taken into account. In this paper, we use local similarity indices to estimate the likelihood of the existence of links in weighted networks, including Common Neighbor, Adamic-Adar Index, Resource Allocation Index, and their weighted versions. In both the unweighted and weighted cases, the resource allocation index performs the best. To our surprise, the weighted indices perform worse, which reminds us of the well-known Weak Tie Theory. Further extensive experimental study shows that the weak ties play a significant role in the link prediction problem, and to emphasize the contribution of weak ties can remarkably enhance the predicting accuracy.