  Completing missing facts is a fundamental task for temporal knowledge graphs
(TKGs). Recently, graph neural network (GNN) based methods, which can
simultaneously explore topological and temporal information, have become the
state-of-the-art (SOTA) to complete TKGs. However, these studies are based on
hand-designed architectures and fail to explore the diverse topological and
temporal properties of TKG. To address this issue, we propose to use neural
architecture search (NAS) to design data-specific message passing architecture
for TKG completion. In particular, we develop a generalized framework to
explore topological and temporal information in TKGs. Based on this framework,
we design an expressive search space to fully capture various properties of
different TKGs. Meanwhile, we adopt a search algorithm, which trains a supernet
structure by sampling single path for efficient search with less cost. We
further conduct extensive experiments on three benchmark datasets. The results
show that the searched architectures by our method achieve the SOTA
performances. Besides, the searched models can also implicitly reveal diverse
properties in different TKGs. Our code is released in
https://github.com/striderdu/SPA.
