Affiliations: [a] College of Computer Science and Engineering, Shandong University of Science and Technology, Jinan, China
| [b] Department of Management Information Systems, National Chung Hsing University, Taichung City, Taiwan
| [c] Department of Computer Science, California State University, Fresno, CA, USA
Correspondence:
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Corresponding author. Meng-Hsiun Tsai, Department of Management Information Systems, National Chung Hsing University, Taichung City, Taiwan. E-mail: [email protected].
Abstract: Estimating similarity using multiple similarity measures or machine learning prediction models is a popular solution to the link prediction problem. The Relation Pattern Deep Learning Classification (RPDLC) technique is proposed in this study, and it is based on multiple neighbor-based similarity metrics and convolution neural networks. The RPDLC first calculates the characteristics for a pair of nodes using neighbor-based metrics and impact nodes. Second, the RPDLC creates a heat map using node characteristics to assess the similarity of the nodes’ connection patterns. Third, the RPDLC uses convolution neural network architecture to build a prediction model for missing relationship prediction. On three separate social network datasets, this method is compared to other state-of-the-art algorithms. On all three datasets, the suggested method achieves the greatest AUC, hovering around 99 percent. The use of convolution neural networks and features via relational patterns to create a prediction model are the paper’s primary contributions.
Keywords: Link prediction problem, convolution neural network, relation pattern, social network
