# Collaborative Filtering Approach to Link Prediction

arXiv: https://arxiv.org/abs/2103.09907 · HTML: https://mlsanity.com/paper/2103.09907/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [The maximum capability of a topological feature in link prediction](https://mlsanity.com/paper/2206.15101/) (2022)
- [A modified gravity model based on network efficiency for vital nodes identification in complex networks](https://mlsanity.com/paper/2111.01526/) (2021)
- [Evaluating link prediction: New perspectives and recommendations](https://mlsanity.com/paper/2502.12777/) (2025)
- [Directed Link Prediction using GNN with Local and Global Feature Fusion](https://mlsanity.com/paper/2506.20235/) (2025)

## Abstract (arXiv)

Link prediction is a fundamental challenge in network science. Among various methods, local similarity indices are widely used for their high cost-performance. However, the performance is less robust: for some networks local indices are highly competitive to state-of-the-art algorithms while for some other networks they are very poor. Inspired by techniques developed for recommender systems, we propose an enhancement framework for local indices based on collaborative filtering (CF). Considering the delicate but important difference between personalized recommendation and link prediction, we further propose an improved framework named as self-included collaborative filtering (SCF). The SCF framework significantly improved the accuracy and robustness of well-known local indices. The combination of SCF framework and a simple local index can produce an index with competitive performance and much lower complexity compared with elaborately-designed state-of-the-art algorithms.