# Pairwise Learning for Neural Link Prediction

arXiv: https://arxiv.org/abs/2112.02936 · HTML: https://mlsanity.com/paper/2112.02936/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [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)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [KBGAN: Adversarial Learning for Knowledge Graph Embeddings](https://mlsanity.com/paper/1711.04071/) (2017)
- [Incorporating GAN for Negative Sampling in Knowledge Representation Learning](https://mlsanity.com/paper/1809.11017/) (2018)

## Built on by (most cited first, 23 of 23)

- [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)
- [Linkless Link Prediction via Relational Distillation](https://mlsanity.com/paper/2210.05801/) (2022)
- [FakeEdge: Alleviate Dataset Shift in Link Prediction](https://mlsanity.com/paper/2211.15899/) (2022)
- [Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning](https://mlsanity.com/paper/2501.01124/) (2025)
- [Link Prediction with Non-Contrastive Learning](https://mlsanity.com/paper/2211.14394/) (2022)
- [Flashlight: Scalable Link Prediction with Effective Decoders](https://mlsanity.com/paper/2209.10100/) (2022)
- [Efficient Link Prediction via GNN Layers Induced by Negative Sampling](https://mlsanity.com/paper/2310.09516/) (2023)
- [GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction](https://mlsanity.com/paper/2210.01301/) (2022)
- [You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction](https://mlsanity.com/paper/2302.14189/) (2023)
- [Node Duplication Improves Cold-start Link Prediction](https://mlsanity.com/paper/2402.09711/) (2024)
- [Reconsidering the Performance of GAE in Link Prediction](https://mlsanity.com/paper/2411.03845/) (2024)
- [Path-aware Siamese Graph Neural Network for Link Prediction](https://mlsanity.com/paper/2208.05781/) (2022)
- [Privacy-Preserving Graph Embedding based on Local Differential Privacy](https://mlsanity.com/paper/2310.11060/) (2023)
- [Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods](https://mlsanity.com/paper/2411.14711/) (2024)
- [Ensemble Learning for Graph Neural Networks](https://mlsanity.com/paper/2310.14166/) (2023)
- [GRANDE: a neural model over directed multigraphs with application to anti-money laundering](https://mlsanity.com/paper/2302.02101/) (2023)
- [OCN: Effectively Utilizing Higher-Order Common Neighbors for Better Link Prediction](https://mlsanity.com/paper/2505.19719/) (2025)
- [Rethinking the Setting of Semi-supervised Learning on Graphs](https://mlsanity.com/paper/2205.14403/) (2022)
- [Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction](https://mlsanity.com/paper/2307.08877/) (2023)
- [Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?](https://mlsanity.com/paper/2309.06574/) (2023)
- [STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning](https://mlsanity.com/paper/2506.19883/) (2025)
- [Scalable Pairwise Kernel Learning with Stochastic Vec Trick](https://mlsanity.com/paper/2606.16979/) (2026)

## Abstract (arXiv)

In this paper, we aim at providing an effective Pairwise Learning Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function. The framework is flexible that any generic graph neural convolution or link prediction specific neural architecture could be employed as neighborhood encoder. For link predictor, we design different scoring functions, which could be selected based on different types of graphs. In negative sampler, we provide several sampling strategies, which are problem specific. As for objective function, we propose to use an effective ranking loss, which approximately maximizes the standard ranking metric AUC. We evaluate the proposed PLNLP framework on 4 link property prediction datasets of Open Graph Benchmark, including ogbl-ddi, ogbl-collab, ogbl-ppa and ogbl-ciation2. PLNLP achieves top 1 performance on ogbl-ddi and ogbl-collab, and top 2 performance on ogbl-ciation2 only with basic neural architecture. The performance demonstrates the effectiveness of PLNLP.