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

arXiv: https://arxiv.org/abs/1909.00174 · HTML: https://mlsanity.com/paper/1909.00174/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [The maximum capability of a topological feature in link prediction](https://mlsanity.com/paper/2206.15101/) (2022)
- [Variational Disentangled Graph Auto-Encoders for Link Prediction](https://mlsanity.com/paper/2306.11315/) (2023)
- [A Network Science perspective of Graph Convolutional Networks: A survey](https://mlsanity.com/paper/2301.04824/) (2023)
- [A Review of Link Prediction Applications in Network Biology](https://mlsanity.com/paper/2312.01275/) (2023)
- [H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction](https://mlsanity.com/paper/2605.02150/) (2026)

## Abstract (arXiv)

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.