  In this paper, we apply an efficient top-$k$ shortest distance routing
algorithm to the link prediction problem and test its efficacy. We compare the
results with other base line and state-of-the-art methods as well as with the
shortest path. Our results show that using top-$k$ distances as a similarity
measure outperforms classical similarity measures such as Jaccard and
Adamic/Adar.
