  Link prediction plays an important role in network analysis and applications.
Recently, approaches for link prediction have evolved from traditional
similarity-based algorithms into embedding-based algorithms. However, most
existing approaches fail to exploit the fact that real-world networks are
different from random networks. In particular, real-world networks are known to
contain motifs, natural network building blocks reflecting the underlying
network-generating processes. In this paper, we propose a novel embedding
algorithm that incorporates network motifs to capture higher-order structures
in the network. To evaluate its effectiveness for link prediction, experiments
were conducted on three types of networks: social networks, biological
networks, and academic networks. The results demonstrate that our algorithm
outperforms both the traditional similarity-based algorithms by 20% and the
state-of-the-art embedding-based algorithms by 19%.
