- Title
- MODEL : motif-based deep feature learning for link prediction
- Creator
- Wang, Lei; Ren, Jing; Xu, Bo; Li, Jianxin; Luo, Wei; Xia, Feng
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171948
- Identifier
- vital:14464
- Identifier
-
https://doi.org/10.1109/TCSS.2019.2962819
- Identifier
- ISBN:2329-924X (ISSN)
- 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.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Computational Social Systems Vol. 7, no. 2 (2020), p. 503-516
- Rights
- Copyright Institute of Electrical and Electronics Engineers Inc.
- Rights
- This metadata is freely available under a CCO license
- Rights
- Open Access
- Subject
- 0801 Artificial Intelligence and Image Processing; 0906 Electrical and Electronic Engineering; Autoencoder; Deep learning; Link prediction; Network embedding; Network motif
- Full Text
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