# LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation

arXiv: https://arxiv.org/abs/2210.10436 · HTML: https://mlsanity.com/paper/2210.10436/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 25 of 25)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Combining Label Propagation and Simple Models Out-performs Graph Neural Networks](https://mlsanity.com/paper/2010.13993/) (2020)
- [Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment](https://mlsanity.com/paper/1611.03954/) (2016)
- [Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding](https://mlsanity.com/paper/1708.05045/) (2017)
- [A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs](https://mlsanity.com/paper/2003.07743/) (2020)
- [Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/1908.08210/) (2019)
- [Unifying Graph Convolutional Neural Networks and Label Propagation](https://mlsanity.com/paper/2002.06755/) (2020)
- [Multi-Channel Graph Neural Network for Entity Alignment](https://mlsanity.com/paper/1908.09898/) (2019)
- [Deep Graph Matching Consensus](https://mlsanity.com/paper/2001.09621/) (2020)
- [Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs](https://mlsanity.com/paper/1905.04914/) (2019)
- [Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network](https://mlsanity.com/paper/1905.11605/) (2019)
- [Relational Reflection Entity Alignment](https://mlsanity.com/paper/2008.07962/) (2020)
- [Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment](https://mlsanity.com/paper/1806.06478/) (2018)
- [Aligning Cross-Lingual Entities with Multi-Aspect Information](https://mlsanity.com/paper/1910.06575/) (2019)
- [Neighborhood Matching Network for Entity Alignment](https://mlsanity.com/paper/2005.05607/) (2020)
- [TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs](https://mlsanity.com/paper/2004.13579/) (2020)
- [Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining](https://mlsanity.com/paper/2103.15452/) (2021)
- [Knowledge Association with Hyperbolic Knowledge Graph Embeddings](https://mlsanity.com/paper/2010.02162/) (2020)
- [GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2010.01447/) (2020)
- [LargeEA: Aligning Entities for Large-scale Knowledge Graphs](https://mlsanity.com/paper/2108.05211/) (2021)
- [From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment](https://mlsanity.com/paper/2109.02363/) (2021)
- [ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities](https://mlsanity.com/paper/2205.10312/) (2022)
- [Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment](https://mlsanity.com/paper/2001.08728/) (2020)

## Built on by (most cited first, 18 of 18)

- [A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment](https://mlsanity.com/paper/2305.06574/) (2023)
- [Neuro-Symbolic Entity Alignment via Variational Inference](https://mlsanity.com/paper/2410.04153/) (2024)
- [Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases](https://mlsanity.com/paper/2403.10978/) (2024)
- [Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment](https://mlsanity.com/paper/2307.02075/) (2023)
- [Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths](https://mlsanity.com/paper/2310.05364/) (2023)
- [Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding](https://mlsanity.com/paper/2401.12798/) (2024)
- [Aligning Multiple Knowledge Graphs in a Single Pass](https://mlsanity.com/paper/2408.00662/) (2024)
- [An Effective and Efficient Time-aware Entity Alignment Framework via Two-aspect Three-view Label Propagation](https://mlsanity.com/paper/2307.06013/) (2023)
- [Unsupervised Deep Cross-Language Entity Alignment](https://mlsanity.com/paper/2309.10598/) (2023)
- [NALA: an Effective and Interpretable Entity Alignment Method](https://mlsanity.com/paper/2404.11968/) (2024)
- [Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model](https://mlsanity.com/paper/2512.07232/) (2025)
- [Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment](https://mlsanity.com/paper/2305.01556/) (2023)
- [OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment](https://mlsanity.com/paper/2305.01561/) (2023)
- [Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation](https://mlsanity.com/paper/2402.03025/) (2024)
- [Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts](https://mlsanity.com/paper/2407.15588/) (2024)
- [DERA: Dense Entity Retrieval for Entity Alignment in Knowledge Graphs](https://mlsanity.com/paper/2408.01154/) (2024)
- [Unsupervised Entity Alignment Based on Personalized Discriminative Rooted Tree](https://mlsanity.com/paper/2502.10044/) (2025)
- [Towards Effective and Efficient Graph Alignment without Supervision](https://mlsanity.com/paper/2603.08526/) (2026)

## Abstract (arXiv)

Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step of bridging and integrating multi-source KGs. In this paper, we argue that existing GNN-based EA methods inherit the inborn defects from their neural network lineage: weak scalability and poor interpretability. Inspired by recent studies, we reinvent the Label Propagation algorithm to effectively run on KGs and propose a non-neural EA framework -- LightEA, consisting of three efficient components: (i) Random Orthogonal Label Generation, (ii) Three-view Label Propagation, and (iii) Sparse Sinkhorn Iteration. According to the extensive experiments on public datasets, LightEA has impressive scalability, robustness, and interpretability. With a mere tenth of time consumption, LightEA achieves comparable results to state-of-the-art methods across all datasets and even surpasses them on many.