Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MODEL: Motif-based Deep Feature Learning for Link Prediction   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%.

33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph