# Link Prediction in Complex Networks: A Mutual Information Perspective

arXiv: https://arxiv.org/abs/1405.4341 · HTML: https://mlsanity.com/paper/1405.4341/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2014.

## Built on by (most cited first, 6 of 6)

- [Parallel Machine Learning for Forecasting the Dynamics of Complex Networks](https://mlsanity.com/paper/2108.12129/) (2021)
- [Forecasting Financial Market Structure from Network Features using Machine Learning](https://mlsanity.com/paper/2110.11751/) (2021)
- [Love tHy Neighbour: Remeasuring Local Structural Node Similarity in Hypergraph-Derived Networks](https://mlsanity.com/paper/2111.00256/) (2021)
- [Completing Networks by Learning Local Connection Patterns](https://mlsanity.com/paper/2204.11852/) (2022)
- [The Ties that matter: From the perspective of Similarity Measure in Online Social Networks](https://mlsanity.com/paper/2212.10960/) (2022)
- [A Review of Link Prediction Applications in Network Biology](https://mlsanity.com/paper/2312.01275/) (2023)

## Abstract (arXiv)

Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.