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Open AccessArticle

Link Prediction Based on Deep Convolutional Neural Network

College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
College of Computer Science and Technology, Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Author to whom correspondence should be addressed.
Information 2019, 10(5), 172;
Received: 28 February 2019 / Revised: 25 April 2019 / Accepted: 5 May 2019 / Published: 9 May 2019
In 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
Keywords: link prediction; network representation learning; deep learning; residual network; attention mechanism link prediction; network representation learning; deep learning; residual network; attention mechanism
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Wang, W.; Wu, L.; Huang, Y.; Wang, H.; Zhu, R. Link Prediction Based on Deep Convolutional Neural Network. Information 2019, 10, 172.

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