MODEL: Motif-based Deep Feature Learning for Link Prediction

• URL: http://arxiv.org/abs/2008.03637v1
• Date: Sun, 9 Aug 2020 03:39:28 GMT
• Title: MODEL: Motif-based Deep Feature Learning for Link Prediction
• Authors: Lei Wang, Jing Ren, Bo Xu, Jianxin Li, Wei Luo, Feng Xia
• Abstract summary: We propose a novel embedding algorithm that incorporates network motifs to capture higher-order structures in the network. Experiments were conducted on three types of networks: social networks, biological networks, and academic networks. Our algorithm outperforms both the traditional similarity-based algorithms by 20% and the state-of-the-art embedding-based algorithms by 19%.
• Score: 23.12527010960999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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%.

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