# Review on Learning and Extracting Graph Features for Link Prediction

## TL;DR

This paper provides a comprehensive review of various methods for link prediction in complex networks, categorizing them into four main types and discussing datasets and future research directions.

## Contribution

It offers an extensive categorization and analysis of state-of-the-art link prediction methods and compiles relevant datasets for further study.

## Key findings

- Categorized link prediction methods into four main groups.
- Reviewed datasets available for link prediction research.
- Discussed recent advances and future challenges in the field.

## Abstract

Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions.