MODEL: Motif-based Deep Feature Learning for Link Prediction
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%.
Cite
@article{arxiv.2008.03637,
title = {MODEL: Motif-based Deep Feature Learning for Link Prediction},
author = {Lei Wang and Jing Ren and Bo Xu and Jianxin Li and Wei Luo and Feng Xia},
journal= {arXiv preprint arXiv:2008.03637},
year = {2020}
}
Comments
14 pages, 7 figures