  Conventional representation learning algorithms for knowledge graphs (KG) map
each entity to a unique embedding vector, ignoring the rich information
contained in the neighborhood. We propose a method named StarGraph, which gives
a novel way to utilize the neighborhood information for large-scale knowledge
graphs to obtain entity representations. An incomplete two-hop neighborhood
subgraph for each target node is at first generated, then processed by a
modified self-attention network to obtain the entity representation, which is
used to replace the entity embedding in conventional methods. We achieved SOTA
performance on ogbl-wikikg2 and got competitive results on fb15k-237. The
experimental results proves that StarGraph is efficient in parameters, and the
improvement made on ogbl-wikikg2 demonstrates its great effectiveness of
representation learning on large-scale knowledge graphs. The code is now
available at \url{https://github.com/hzli-ucas/StarGraph}.
