  Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is
the pivotal step to KGs integration, also known as \emph{entity alignment}
(EA). However, most existing EA methods are inefficient and poor in
scalability. A recent summary points out that some of them even require several
days to deal with a dataset containing 200,000 nodes (DWY100K). We believe
over-complex graph encoder and inefficient negative sampling strategy are the
two main reasons. In this paper, we propose a novel KG encoder -- Dual
Attention Matching Network (Dual-AMN), which not only models both intra-graph
and cross-graph information smartly, but also greatly reduces computational
complexity. Furthermore, we propose the Normalized Hard Sample Mining Loss to
smoothly select hard negative samples with reduced loss shift. The experimental
results on widely used public datasets indicate that our method achieves both
high accuracy and high efficiency. On DWY100K, the whole running process of our
method could be finished in 1,100 seconds, at least 10* faster than previous
work. The performances of our method also outperform previous works across all
datasets, where Hits@1 and MRR have been improved from 6% to 13%.
