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 promote graph homophily. The discretization of the gradient flow produces a fast and scalable approach, termed Triple Feature Propagation (TFP). TFP innovatively channels gradient flow through three views: entity-to-entity, entity-to-relation, and relation-to-entity. This generalized gradient flow enables TFP to harness the multi-view structural information of KGs. Rigorous experimentation on diverse real-world 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（Entity Alignment, EA）作为整合多源知识图谱（Knowledge Graphs, KGs）的关键过程，旨在识别这些图谱间的等价实体对。现有方法大多将EA视为图表示学习任务，侧重于增强图编码器。然而，EA中的解码过程——对有效操作和对齐精度至关重要——却鲜受关注，且仍局限于特定数据集和模型架构，需同时利用实体及额外的显式关系嵌入。这种特异性限制了其适用性，尤其在基于图神经网络（GNN）的模型中。为弥补这一不足，我们提出一种新颖、通用且高效的EA解码方法，该方法仅依赖实体嵌入。我们的方法通过最小化狄利克雷能量（Dirichlet energy）来优化解码过程，从而引发图中的梯度流（gradient flow），促进图同质性（graph homophily）。对该梯度流的离散化产生了一种快速且可扩展的方法，称为三重特征传播（Triple Feature Propagation, TFP）。TFP创新性地通过三种视角引导梯度流：实体到实体、实体关系到关系到实体。这种广义的梯度流使TFP能够利用KGs的多视图结构信息。在多样化真实世界数据集上的严格实验表明，我们的方法显著提升了多种EA方法的性能。值得注意的是，该方法在实现这些进步的同时，额外计算时间不超过6秒，为未来EA方法在效率与适应性方面树立了新标杆。