  Knowledge graphs contain rich knowledge about various entities and the
relational information among them, while temporal knowledge graphs (TKGs)
describe and model the interactions of the entities over time. In this context,
automatic temporal knowledge graph completion (TKGC) has gained great interest.
Recent TKGC methods integrate advanced deep learning techniques, e.g.,
Transformers, and achieve superior model performance. However, this also
introduces a large number of excessive parameters, which brings a heavier
burden for parameter optimization. In this paper, we propose a simple but
powerful graph encoder for TKGC, called TARGCN. TARGCN is parameter-efficient,
and it extensively explores every entity's temporal context for learning
contextualized representations. We find that instead of adopting various kinds
of complex modules, it is more beneficial to efficiently capture the temporal
contexts of entities. We experiment TARGCN on three benchmark datasets. Our
model can achieve a more than 46% relative improvement on the GDELT dataset
compared with state-of-the-art TKGC models. Meanwhile, it outperforms the
strongest baseline on the ICEWS05-15 dataset with around 18% fewer parameters.
