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 found successful applications on disparate fields. An intuitive consensus is that two nodes sharing common neighbors are likely to have a link, while some recent evidences indicate 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 137 real networks. Overall speaking, the class of Cannistraci–Hebb indices performs the best among all considered candidates. In addition, 3-hop-based indices outperform 2-hop-based indices on ROC-AUC, and 3-hop-based indices and 2-hop-based indices are competitive on precision. Further statistical results show that 3-hop-based indices are more suitable for disassortative networks with lower densities and lower average clustering coefficients.

链接预测是网络科学中一项重要且具有挑战性的任务。大多数已知的方法都是基于相似度的，它们为节点对分配相似度索引，并假定相似度较大的两个节点通过链接进行连接的可能性较高。由于基于简单性，可解释性和高效率，基于相似性的方法（尤其是仅基于本地信息的相似性方法）已成功应用于不同领域。一个直观的共识是，共享公共邻居的两个节点很可能具有链接，而一些最新证据表明，三跳路径的数量比公共邻居的数量更准确地预测了丢失的链接。在本文中，我们对137个真实网络上的基于2跳和基于3跳的相似性指标进行了广泛的实验比较。总体而言，Cannistraci–Hebb指数的类别在所有考虑的候选人中表现最好。此外，基于3跳的索引在ROC-AUC上优于基于2跳的索引，基于3跳的索引和基于2跳的索引在精度上具有竞争力。进一步的统计结果表明，基于3跳的索引更适合于具有较低密度和较低平均聚类系数的分解网络。