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MODEL : motif-based deep feature learning for link prediction
Authors
Jianxin Li
Wei Luo
+4 more
Jing Ren
Lei Wang
Feng Xia
Bo Xu
Publication date
1 January 2020
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
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on
arXiv
Abstract
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 article, 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%). © 2014 IEEE
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Deakin Research Online
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