Experimental analyses on 2-hop-based and 3-hop-based link prediction algorithms

TL;DR

This paper compares 2-hop and 3-hop similarity-based link prediction algorithms across 128 real networks, finding 3-hop indices slightly outperform 2-hop ones, with Cannistraci-Hebb indices being the most effective.

Contribution

It provides a comprehensive experimental comparison of 2-hop and 3-hop link prediction methods, highlighting the relative performance of various indices on real-world networks.

Findings

3-hop indices outperform 2-hop indices with 55.88% success rate

Cannistraci-Hebb indices perform best among tested methods

Performance depends on specific network characteristics

Abstract

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.