Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs), seeks to identify equivalent entity pairs across these graphs. Most existing approaches regard EA as a graph representation learning task, concentrating on enhancing graph encoders. However, the decoding process in EA - essential for effective operation and alignment accuracy - has received limited attention and remains tailored to specific datasets and model architectures, necessitating both entity and additional explicit relation embeddings. This specificity limits its applicability, particularly in GNN-based models. To address this gap, we introduce a novel, generalized, and efficient decoding approach for EA, relying solely on entity embeddings. Our method optimizes the decoding process by minimizing Dirichlet energy, leading to the gradient flow within the graph, to maximize graph homophily. The discretization of the gradient flow produces a fast and scalable approach, termed Triple Feature Propagation (TFP). TFP innovatively generalizes adjacency matrices to multi-views matrices:entity-to-entity, entity-to-relation, relation-to-entity, and relation-to-triple. The gradient flow through generalized matrices enables TFP to harness the multi-view structural information of KGs. Rigorous experimentation on diverse public datasets demonstrates that our approach significantly enhances various EA methods. Notably, the approach achieves these advancements with less than 6 seconds of additional computational time, establishing a new benchmark in efficiency and adaptability for future EA methods.

翻译：实体对齐（EA）作为多源知识图谱（KGs）集成中的关键过程，旨在识别跨图谱的等价实体对。现有方法多将EA视为图表示学习任务，聚焦于增强图编码器。然而，EA中解码过程——对有效操作和对齐精度至关重要——却鲜受关注，且仍依赖于特定数据集和模型架构，需同时使用实体嵌入与额外的显式关系嵌入。这种特异性限制了其适用性，尤其在基于GNN的模型中。为填补这一空白，我们提出一种新颖、通用且高效的EA解码方法，仅依赖实体嵌入。该方法通过最小化狄利克雷能量，引导图内梯度流，最大化图同质性，从而优化解码过程。梯度流的离散化产生一种快速可扩展的方法，称为三重特征传播（TFP）。TFP创新地将邻接矩阵泛化为多视图矩阵：实体-实体、实体-关系、关系-实体及关系-三元组。通过广义矩阵的梯度流，TFP能利用知识图谱的多视图结构信息。在多种公开数据集上的严格实验表明，我们的方法可显著增强各类EA方法。尤为值得注意的是，该方法额外计算时间不足6秒，却实现了这些改进，为未来EA方法在效率与适应性方面树立了新基准。