  Link prediction is a significant and challenging task in network science. The
majority of known methods are similarity-based, which assign similarity indices
for node pairs and assume that two nodes of larger similarity have higher
probability to be connected by a link. Due to their simplicity,
interpretability and high efficiency, similarity-based methods, in particular
those based only on local information, have already found successful
applications on disparate fields. In this research domain, an intuitive
consensus is that two nodes sharing common neighbors are very likely to have a
link, while some recent evidences argue that the number of 3-hop paths more
accurately predicts missing links than the number of common neighbors. In this
paper, we implement extensive experimental comparisons between 2-hop-based and
3-hop-based similarity indices on 128 real networks. Our results indicate that
the 3-hop-based indices perform slightly better with a winning rate about
55.88%, but which index is the best one still depends on the target network.
Overall speaking, the class of Cannistraci-Hebb indices performs the best among
all considered candidates.

Physics and Society;Social and Information Networks