# What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings

arXiv: https://arxiv.org/abs/2306.02622 · HTML: https://mlsanity.com/paper/2306.02622/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 44 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [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)
- [Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation](https://mlsanity.com/paper/1911.08936/) (2019)
- [Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs](https://mlsanity.com/paper/1905.04914/) (2019)
- [Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment](https://mlsanity.com/paper/1806.06478/) (2018)
- [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)
- [SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs](https://mlsanity.com/paper/2203.01044/) (2022)
- [A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning](https://mlsanity.com/paper/2103.15059/) (2021)
- [Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs](https://mlsanity.com/paper/2203.02150/) (2022)
- [ActiveEA: Active Learning for Neural Entity Alignment](https://mlsanity.com/paper/2110.06474/) (2021)
- [Transforming PageRank into an Infinite-Depth Graph Neural Network](https://mlsanity.com/paper/2207.00684/) (2022)
- [LinkNBed: Multi-Graph Representation Learning with Entity Linkage](https://mlsanity.com/paper/1807.08447/) (2018)
- [Knowledge Embedding Based Graph Convolutional Network](https://mlsanity.com/paper/2006.07331/) (2020)
- [Active Learning for Entity Alignment](https://mlsanity.com/paper/2001.08943/) (2020)

## Built on by (most cited first, 4 of 4)

- [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](https://mlsanity.com/paper/2402.05391/) (2024)
- [LLM+KG@VLDB'24 Workshop Summary](https://mlsanity.com/paper/2410.01978/) (2024)
- [Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production](https://mlsanity.com/paper/2410.18475/) (2024)
- [Harnessing Structural Context for Entity Alignment Foundation Models](https://mlsanity.com/paper/2606.06109/) (2026)

## Abstract (arXiv)

Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a critical step in this process. Despite recent considerable research progress in embedding-based EA, how it works remains to be explored. In this paper, we provide a similarity flooding perspective to explain existing translation-based and aggregation-based EA models. We prove that the embedding learning process of these models actually seeks a fixpoint of pairwise similarities between entities. We also provide experimental evidence to support our theoretical analysis. We propose two simple but effective methods inspired by the fixpoint computation in similarity flooding, and demonstrate their effectiveness on benchmark datasets. Our work bridges the gap between recent embedding-based models and the conventional similarity flooding algorithm. It would improve our understanding of and increase our faith in embedding-based EA.