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 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.
In particular in REF is shown how the accuracy in link prediction can be improved by exploiting the contribution of weak ties.
Role of weak ties in link prediction of complex networks
ArXiv
ArXiv
Computer Science