@inproceedings{wang-etal-2025-rethinking,
    title = "Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding",
    author = "Wang, Yuanyi  and
      Li, Han  and
      Sun, Haifeng  and
      Zhang, Lei  and
      He, Bo  and
      Tang, Wei  and
      Yan, Tianhao  and
      Qi, Qi  and
      Wang, Jingyu",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.255/",
    pages = "4521--4535",
    ISBN = "979-8-89176-195-7",
    abstract = "Entity alignment (EA) is crucial for integrating multi-source knowledge graphs (KGs), aiming to identify equivalent entities across different graphs. However, most existing EA decoding methods rely on both entity and relation embeddings, limiting their generalizability and efficiency, especially in GNN-based models. To address these challenges, we propose Triple Feature Propagation (TFP), an adaptable and fast EA decoding framework that only utilizes entity embeddings. TFP reconstructs KG representation by maximizing the smoothness of entity embeddings. The discretized smoothness-maximization process yields the explicit Euler solution of TFP. We also generalize multi-view matrices: entity-to-entity, entity-to-relation, relation-to-entity, and relation-to-triple, to capture structural diversity. Extensive experiments on public datasets demonstrate that TFP is fast and adaptable to various encoders, achieving comparable results to state-of-the-art methods in under 6 seconds, and surpassing them in many cases."
}