# Evaluating Link Prediction Methods

arXiv: https://arxiv.org/abs/1505.04094 · HTML: https://mlsanity.com/paper/1505.04094/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2015.

## Built on by (most cited first, 30 of 31)

- [Towards Better Evaluation for Dynamic Link Prediction](https://mlsanity.com/paper/2207.10128/) (2022)
- [Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking](https://mlsanity.com/paper/2306.10453/) (2023)
- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics](https://mlsanity.com/paper/2502.02975/) (2025)
- [Universal Link Predictor By In-Context Learning on Graphs](https://mlsanity.com/paper/2402.07738/) (2024)
- [FakeEdge: Alleviate Dataset Shift in Link Prediction](https://mlsanity.com/paper/2211.15899/) (2022)
- [Multi-label Node Classification On Graph-Structured Data](https://mlsanity.com/paper/2304.10398/) (2023)
- [A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training](https://mlsanity.com/paper/2206.05555/) (2022)
- [What Do Temporal Graph Learning Models Learn?](https://mlsanity.com/paper/2510.09416/) (2025)
- [Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks](https://mlsanity.com/paper/2109.09190/) (2021)
- [Deep Ensembles for Graphs with Higher-order Dependencies](https://mlsanity.com/paper/2205.13988/) (2022)
- [The maximum capability of a topological feature in link prediction](https://mlsanity.com/paper/2206.15101/) (2022)
- [Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science](https://mlsanity.com/paper/2412.09628/) (2024)
- [Link Prediction in Bipartite Networks](https://mlsanity.com/paper/2406.06658/) (2024)
- [Predicting Links on Wikipedia with Anchor Text Information](https://mlsanity.com/paper/2105.11734/) (2021)
- [An Approach for Link Prediction in Directed Complex Networks based on Asymmetric Similarity-Popularity](https://mlsanity.com/paper/2207.07399/) (2022)
- [DBGDGM: Dynamic Brain Graph Deep Generative Model](https://mlsanity.com/paper/2301.11408/) (2023)
- [New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic Graphs](https://mlsanity.com/paper/2311.18486/) (2023)
- [Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms](https://mlsanity.com/paper/2405.17182/) (2024)
- [Multi-Label Node Classification with Label Influence Propagation](https://mlsanity.com/paper/2607.00671/) (2026)
- [A Complex Network based Graph Embedding Method for Link Prediction](https://mlsanity.com/paper/2209.04884/) (2022)
- [AMFPMC -- An improved method of detecting multiple types of drug-drug interactions using only known drug-drug interactions](https://mlsanity.com/paper/2302.03355/) (2023)
- [Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features](https://mlsanity.com/paper/2410.04251/) (2024)
- [Rethinking Link Prediction for Directed Graphs](https://mlsanity.com/paper/2502.05724/) (2025)
- [Evaluating link prediction: New perspectives and recommendations](https://mlsanity.com/paper/2502.12777/) (2025)
- [Node2Vec-DGI-EL: A Hierarchical Graph Representation Learning Model for Ingredient-Disease Association Prediction](https://mlsanity.com/paper/2505.00236/) (2025)
- [Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge](https://mlsanity.com/paper/2507.05540/) (2025)
- [Enhancing Contrastive Link Prediction With Edge Balancing Augmentation](https://mlsanity.com/paper/2508.14808/) (2025)
- [H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction](https://mlsanity.com/paper/2605.02150/) (2026)
- [Applications of temporal graph learning for predicting the dynamics of biological systems](https://mlsanity.com/paper/2605.28659/) (2026)

## Abstract (arXiv)

Link prediction is a popular research area with important applications in a variety of disciplines, including biology, social science, security, and medicine. The fundamental requirement of link prediction is the accurate and effective prediction of new links in networks. While there are many different methods proposed for link prediction, we argue that the practical performance potential of these methods is often unknown because of challenges in the evaluation of link prediction, which impact the reliability and reproducibility of results. We describe these challenges, provide theoretical proofs and empirical examples demonstrating how current methods lead to questionable conclusions, show how the fallacy of these conclusions is illuminated by methods we propose, and develop recommendations for consistent, standard, and applicable evaluation metrics. We also recommend the use of precision-recall threshold curves and associated areas in lieu of receiver operating characteristic curves due to complications that arise from extreme imbalance in the link prediction classification problem.