# PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction

arXiv: https://arxiv.org/abs/2302.12465 · HTML: https://mlsanity.com/paper/2302.12465/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 57 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 30 of 33)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [On Explainability of Graph Neural Networks via Subgraph Explorations](https://mlsanity.com/paper/2102.05152/) (2021)
- [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)
- [PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2010.05788/) (2020)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2001.06216/) (2020)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [Generative Causal Explanations for Graph Neural Networks](https://mlsanity.com/paper/2104.06643/) (2021)
- [CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks](https://mlsanity.com/paper/2102.03322/) (2021)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation](https://mlsanity.com/paper/2110.15114/) (2021)
- [Learning from Counterfactual Links for Link Prediction](https://mlsanity.com/paper/2106.02172/) (2021)
- [STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1905.13129/) (2019)
- [GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games](https://mlsanity.com/paper/2201.12380/) (2022)
- [Linkless Link Prediction via Relational Distillation](https://mlsanity.com/paper/2210.05801/) (2022)
- [AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search](https://mlsanity.com/paper/2104.12080/) (2021)
- [PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems](https://mlsanity.com/paper/1911.08378/) (2019)

## Built on by (most cited first, 25 of 25)

- [G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation](https://mlsanity.com/paper/2502.12586/) (2025)
- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [Path-based Explanation for Knowledge Graph Completion](https://mlsanity.com/paper/2401.02290/) (2024)
- [DINE: Dimensional Interpretability of Node Embeddings](https://mlsanity.com/paper/2310.01162/) (2023)
- [Rewarding Explainability in Drug Repurposing with Knowledge Graphs](https://mlsanity.com/paper/2509.02276/) (2025)
- [KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion](https://mlsanity.com/paper/2404.03893/) (2024)
- [Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods](https://mlsanity.com/paper/2411.14711/) (2024)
- [Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses](https://mlsanity.com/paper/2508.19641/) (2025)
- [Numerical Literals in Link Prediction: A Critical Examination of Models and Datasets](https://mlsanity.com/paper/2407.18241/) (2024)
- [eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules](https://mlsanity.com/paper/2412.04846/) (2024)
- [Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations](https://mlsanity.com/paper/2503.18001/) (2025)
- [MAPN: Enhancing Heterogeneous Sparse Graph Representation by Mamba-based Asynchronous Aggregation](https://mlsanity.com/paper/2502.16454/) (2025)
- [Unifying Post-hoc Explanations of Knowledge Graph Completions](https://mlsanity.com/paper/2507.22951/) (2025)
- [UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models](https://mlsanity.com/paper/2605.17285/) (2026)
- [Explainable History Distillation by Marked Temporal Point Process](https://mlsanity.com/paper/2311.07797/) (2023)
- [Evaluating graph-based explanations for AI-based recommender systems](https://mlsanity.com/paper/2407.12357/) (2024)
- [How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method](https://mlsanity.com/paper/2411.00612/) (2024)
- [FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths](https://mlsanity.com/paper/2502.16281/) (2025)
- [RAW-Explainer: Post-hoc Explanations of Graph Neural Networks on Knowledge Graphs](https://mlsanity.com/paper/2506.12558/) (2025)
- [Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning](https://mlsanity.com/paper/2508.11943/) (2025)
- [CQD-SHAP: Explainable Complex Query Answering via Shapley Values](https://mlsanity.com/paper/2510.15623/) (2025)
- [A Community-Enhanced Graph Representation Model for Link Prediction](https://mlsanity.com/paper/2512.21166/) (2025)
- [Accelerating Storage-Based Training for Graph Neural Networks](https://mlsanity.com/paper/2601.01473/) (2026)
- [Explaining Synergistic Effects in Social Recommendations](https://mlsanity.com/paper/2601.18151/) (2026)
- [MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation](https://mlsanity.com/paper/2604.03666/) (2026)

## Abstract (arXiv)

Transparency and accountability have become major concerns for black-box machine learning (ML) models. Proper explanations for the model behavior increase model transparency and help researchers develop more accountable models. Graph neural networks (GNN) have recently shown superior performance in many graph ML problems than traditional methods, and explaining them has attracted increased interest. However, GNN explanation for link prediction (LP) is lacking in the literature. LP is an essential GNN task and corresponds to web applications like recommendation and sponsored search on web. Given existing GNN explanation methods only address node/graph-level tasks, we propose Path-based GNN Explanation for heterogeneous Link prediction (PaGE-Link) that generates explanations with connection interpretability, enjoys model scalability, and handles graph heterogeneity. Qualitatively, PaGE-Link can generate explanations as paths connecting a node pair, which naturally captures connections between the two nodes and easily transfer to human-interpretable explanations. Quantitatively, explanations generated by PaGE-Link improve AUC for recommendation on citation and user-item graphs by 9 - 35% and are chosen as better by 78.79% of responses in human evaluation.