mlsanityData to 14 Oct 2026

Progresses and Challenges in Link Prediction

arXiv 2102.11472 ↗First posted 23 Feb 2021physics.data-an cs.IRcited by 14

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 2; 2025: 5.

14 papers.

Sort:

  1. 2024GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Taskscited by 63
  2. 2022Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge networkcited by 36
  3. 2022Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selectioncited by 26
  4. 2021A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphscited by 6
  5. 2023Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphscited by 4
  6. 2025Bridging Theory and Practice in Link Representation with Graph Neural Networkscited by 4
  7. 2022The maximum capability of a topological feature in link predictioncited by 3
  8. 2022Link prediction with continuous-time classical and quantum walkscited by 2
  9. 2024Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariancecited by 2
  10. 2023A Review of Link Prediction Applications in Network Biologycited by 0
  11. 2025Node2Vec-DGI-EL: A Hierarchical Graph Representation Learning Model for Ingredient-Disease Association Predictioncited by 0
  12. 2025GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Predictioncited by 0
2 more
  1. 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0
  2. 2025Oxytrees: Model Trees for Bipartite Learningcited by 0