  Graph neural network (GNN)-based methods have demonstrated remarkable
performance in various knowledge graph (KG) tasks. However, most existing
approaches rely on observing all entities during training, posing a challenge
in real-world knowledge graphs where new entities emerge frequently. To address
this limitation, we introduce Decentralized Attention Network (DAN). DAN
leverages neighbor context as the query vector to score the neighbors of an
entity, thereby distributing the entity semantics only among its neighbor
embeddings. To effectively train a DAN, we introduce self-distillation, a
technique that guides the network in generating desired representations.
Theoretical analysis validates the effectiveness of our approach. We implement
an end-to-end framework and conduct extensive experiments to evaluate our
method, showcasing competitive performance on conventional entity alignment and
entity prediction tasks. Furthermore, our method significantly outperforms
existing methods in open-world settings.
