# NAEx: A Plug-and-Play Framework for Explaining Network Alignment

arXiv: https://arxiv.org/abs/2508.04731 · HTML: https://mlsanity.com/paper/2508.04731/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (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)
- [XGNN: Towards Model-Level Explanations of Graph Neural Networks](https://mlsanity.com/paper/2006.02587/) (2020)
- [GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2001.06216/) (2020)
- [Training Graph Neural Networks with 1000 Layers](https://mlsanity.com/paper/2106.07476/) (2021)
- [REGAL: Representation Learning-based Graph Alignment](https://mlsanity.com/paper/1802.06257/) (2018)
- [A Survey on Explainability of Graph Neural Networks](https://mlsanity.com/paper/2306.01958/) (2023)
- [Generating Explanations to Understand and Repair Embedding-based Entity Alignment](https://mlsanity.com/paper/2312.04877/) (2023)
- [Generative Explanations for Graph Neural Network: Methods and Evaluations](https://mlsanity.com/paper/2311.05764/) (2023)
- [Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation](https://mlsanity.com/paper/2208.11025/) (2022)
- [Towards Higher-order Topological Consistency for Unsupervised Network Alignment](https://mlsanity.com/paper/2208.12463/) (2022)
- [Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors](https://mlsanity.com/paper/2111.11335/) (2021)
- [i-Align: an interpretable knowledge graph alignment model](https://mlsanity.com/paper/2308.13755/) (2023)
- [Modeling of Information Diffusion on Social Networks with Applications to WeChat](https://mlsanity.com/paper/1704.03261/) (2017)

## Abstract (arXiv)

Network alignment (NA) identifies corresponding nodes across multiple networks, with applications in domains like social networks, co-authorship, and biology. Despite advances in alignment models, their interpretability remains limited, making it difficult to understand alignment decisions and posing challenges in building trust, particularly in high-stakes domains. To address this, we introduce NAEx, a plug-and-play, model-agnostic framework that explains alignment models by identifying key subgraphs and features influencing predictions. NAEx addresses the key challenge of preserving the joint cross-network dependencies on alignment decisions by: (1) jointly parameterizing graph structures and feature spaces through learnable edge and feature masks, and (2) introducing an optimization objective that ensures explanations are both faithful to the original predictions and enable meaningful comparisons of structural and feature-based similarities between networks. NAEx is an inductive framework that efficiently generates NA explanations for previously unseen data. We introduce evaluation metrics tailored to alignment explainability and demonstrate NAEx's effectiveness and efficiency on benchmark datasets by integrating it with four representative NA models.