# Progresses and Challenges in Link Prediction

arXiv: https://arxiv.org/abs/2102.11472 · HTML: https://mlsanity.com/paper/2102.11472/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2021.

## Built on by (most cited first, 14 of 14)

- [GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks](https://mlsanity.com/paper/2402.07197/) (2024)
- [Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network](https://mlsanity.com/paper/2210.00881/) (2022)
- [Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection](https://mlsanity.com/paper/2212.12488/) (2022)
- [A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphs](https://mlsanity.com/paper/2112.00075/) (2021)
- [Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2307.08411/) (2023)
- [Bridging Theory and Practice in Link Representation with Graph Neural Networks](https://mlsanity.com/paper/2506.24018/) (2025)
- [The maximum capability of a topological feature in link prediction](https://mlsanity.com/paper/2206.15101/) (2022)
- [Link prediction with continuous-time classical and quantum walks](https://mlsanity.com/paper/2208.11030/) (2022)
- [Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance](https://mlsanity.com/paper/2410.16935/) (2024)
- [A Review of Link Prediction Applications in Network Biology](https://mlsanity.com/paper/2312.01275/) (2023)
- [Node2Vec-DGI-EL: A Hierarchical Graph Representation Learning Model for Ingredient-Disease Association Prediction](https://mlsanity.com/paper/2505.00236/) (2025)
- [GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction](https://mlsanity.com/paper/2507.07138/) (2025)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)
- [Oxytrees: Model Trees for Bipartite Learning](https://mlsanity.com/paper/2511.12713/) (2025)

## Abstract (arXiv)

Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the standard problem and metrics of link prediction, this Perspective will summarize representative progresses about local similarity indices, link predictability, network embedding, matrix completion, ensemble learning and others, mainly extracted from thousands of related publications in the last decade. Finally, this Perspective will outline some long-standing challenges for future studies.