# Revisiting Link Prediction: A Data Perspective

arXiv: https://arxiv.org/abs/2310.00793 · HTML: https://mlsanity.com/paper/2310.00793/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 90 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 30 of 37)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning](https://mlsanity.com/paper/2010.16103/) (2020)
- [Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks](https://mlsanity.com/paper/2203.00199/) (2022)
- [Graph Neural Networks for Link Prediction with Subgraph Sketching](https://mlsanity.com/paper/2209.15486/) (2022)
- [Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2206.04216/) (2022)
- [Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking](https://mlsanity.com/paper/2306.10453/) (2023)
- [Neural Common Neighbor with Completion for Link Prediction](https://mlsanity.com/paper/2302.00890/) (2023)
- [OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs](https://mlsanity.com/paper/2205.15117/) (2022)
- [Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning](https://mlsanity.com/paper/2202.13538/) (2022)
- [Geodesic Graph Neural Network for Efficient Graph Representation Learning](https://mlsanity.com/paper/2210.02636/) (2022)
- [Hashing-Accelerated Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2105.14280/) (2021)
- [Revisiting Neighborhood-based Link Prediction for Collaborative Filtering](https://mlsanity.com/paper/2203.15789/) (2022)
- [Leveraging Node Attributes for Incomplete Relational Data](https://mlsanity.com/paper/1706.04289/) (2017)
- [Cumulative effects of triadic closure and homophily in social networks](https://mlsanity.com/paper/1809.06057/) (2018)
- [Link Prediction via Higher-Order Motif Features](https://mlsanity.com/paper/1902.06679/) (2019)
- [SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation](https://mlsanity.com/paper/2305.09958/) (2023)

## Built on by (most cited first, 29 of 29)

- [Position: Graph Foundation Models are Already Here](https://mlsanity.com/paper/2402.02216/) (2024)
- [KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning](https://mlsanity.com/paper/2406.18380/) (2024)
- [Graph Machine Learning in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2404.14928/) (2024)
- [On the Benefits of Attribute-Driven Graph Domain Adaptation](https://mlsanity.com/paper/2502.06808/) (2025)
- [Efficient Neural Common Neighbor for Temporal Graph Link Prediction](https://mlsanity.com/paper/2406.07926/) (2024)
- [LPFormer: An Adaptive Graph Transformer for Link Prediction](https://mlsanity.com/paper/2310.11009/) (2023)
- [Universal Link Predictor By In-Context Learning on Graphs](https://mlsanity.com/paper/2402.07738/) (2024)
- [Mixture of Link Predictors on Graphs](https://mlsanity.com/paper/2402.08583/) (2024)
- [UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs](https://mlsanity.com/paper/2407.12269/) (2024)
- [On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks](https://mlsanity.com/paper/2409.17475/) (2024)
- [LinkGPT: Teaching Large Language Models To Predict Missing Links](https://mlsanity.com/paper/2406.04640/) (2024)
- [Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models](https://mlsanity.com/paper/2406.01899/) (2024)
- [TFMLinker: Universal Link Predictor by Graph In-Context Learning with Tabular Foundation Models](https://mlsanity.com/paper/2602.08592/) (2026)
- [A Pure Transformer Pretraining Framework on Text-attributed Graphs](https://mlsanity.com/paper/2406.13873/) (2024)
- [Future Link Prediction Without Memory or Aggregation](https://mlsanity.com/paper/2505.19408/) (2025)
- [Reconsidering the Performance of GAE in Link Prediction](https://mlsanity.com/paper/2411.03845/) (2024)
- [AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning](https://mlsanity.com/paper/2604.05846/) (2026)
- [VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models](https://mlsanity.com/paper/2411.14832/) (2024)
- [Unveiling Mode Connectivity in Graph Neural Networks](https://mlsanity.com/paper/2502.12608/) (2025)
- [A Scalable Pretraining Framework for Link Prediction with Efficient Adaptation](https://mlsanity.com/paper/2508.04645/) (2025)
- [EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval](https://mlsanity.com/paper/2604.17458/) (2026)
- [Attribute-Enhanced Similarity Ranking for Sparse Link Prediction](https://mlsanity.com/paper/2412.00261/) (2024)
- [OCN: Effectively Utilizing Higher-Order Common Neighbors for Better Link Prediction](https://mlsanity.com/paper/2505.19719/) (2025)
- [Towards Understanding Link Predictor Generalizability Under Distribution Shifts](https://mlsanity.com/paper/2406.08788/) (2024)
- [Heuristic Methods are Good Teachers to Distill MLPs for Graph Link Prediction](https://mlsanity.com/paper/2504.06193/) (2025)
- [Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment](https://mlsanity.com/paper/2506.07168/) (2025)
- [A Community-Enhanced Graph Representation Model for Link Prediction](https://mlsanity.com/paper/2512.21166/) (2025)
- [Instance Discrimination for Link Prediction](https://mlsanity.com/paper/2605.20257/) (2026)
- [A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks](https://mlsanity.com/paper/2606.22033/) (2026)

## Abstract (arXiv)

Link prediction, a fundamental task on graphs, has proven indispensable in various applications, e.g., friend recommendation, protein analysis, and drug interaction prediction. However, since datasets span a multitude of domains, they could have distinct underlying mechanisms of link formation. Evidence in existing literature underscores the absence of a universally best algorithm suitable for all datasets. In this paper, we endeavor to explore principles of link prediction across diverse datasets from a data-centric perspective. We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity. We then unearth relationships among those factors where (i) global structural proximity only shows effectiveness when local structural proximity is deficient. (ii) The incompatibility can be found between feature and structural proximity. Such incompatibility leads to GNNs for Link Prediction (GNN4LP) consistently underperforming on edges where the feature proximity factor dominates. Inspired by these new insights from a data perspective, we offer practical instruction for GNN4LP model design and guidelines for selecting appropriate benchmark datasets for more comprehensive evaluations.