Link Prediction Based on Deep Convolutional Neural Network
AbstractIn recent years, endless link prediction algorithms based on network representation learning have emerged. Network representation learning mainly constructs feature vectors by capturing the neighborhood structure information of network nodes for link prediction. However, this type of algorithm only focuses on learning topology information from the simple neighbor network node. For example, DeepWalk takes a random walk path as the neighborhood of nodes. In addition, such algorithms only take advantage of the potential features of nodes, but the explicit features of nodes play a good role in link prediction. In this paper, a link prediction method based on deep convolutional neural network is proposed. It constructs a model of the residual attention network to capture the link structure features from the sub-graph. Further study finds that the information flow transmission efficiency of the residual attention mechanism was not high, so a densely convolutional neural network model was proposed for link prediction. We evaluate our proposed method on four published data sets. The results show that our method is better than several other benchmark algorithms on link prediction. View Full-Text
Share & Cite This Article
Wang, W.; Wu, L.; Huang, Y.; Wang, H.; Zhu, R. Link Prediction Based on Deep Convolutional Neural Network. Information 2019, 10, 172.
Wang W, Wu L, Huang Y, Wang H, Zhu R. Link Prediction Based on Deep Convolutional Neural Network. Information. 2019; 10(5):172.Chicago/Turabian Style
Wang, Wentao; Wu, Lintao; Huang, Ye; Wang, Hao; Zhu, Rongbo. 2019. "Link Prediction Based on Deep Convolutional Neural Network." Information 10, no. 5: 172.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.