An Unsupervised Link Prediction Algorithm Based on an ApproximateGraph Neural Network Framework

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An Unsupervised Link Prediction Algorithm Based on an ApproximateGraph Neural Network Framework

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[1]LI Gege,YE Zhonglin,CAO Shujuan,et al.An Unsupervised Link Prediction Algorithm Based on an ApproximateGraph Neural Network Framework[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):75-82.[doi:10.13705/j.issn.1671-6833.2024.03.011]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 06 Page number: 75-82 Column: Public date: 2024-09-25

Title:: An Unsupervised Link Prediction Algorithm Based on an ApproximateGraph Neural Network Framework

Author(s):: LI Gege¹; 2; YE Zhonglin¹; 2; CAO Shujuan¹; 2; ZHOU Lin¹; 2; WANG Xueli¹; 2; 1. College of Computer, Qinghai Normal University, Xining 810008, China; 2. The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining 810008, China

Keywords:: matrix factorization; vector optimization; graph convolutional neural network; similarity matrix; link prediction; high-order neighbors

CLC:: TP393. 0

DOI:: 10.13705/j.issn.1671-6833.2024.03.011

Abstract:: For unlabeled networks, the link prediction method based on graph neural networks had poor performance when using its efficient modeling mechanism for link prediction tasks. An unsupervised link prediction algorithm (ALIP) was proposed. It could approximate the graph neural network framework to simulate the efficientmodeling mechanism and learning process of graph neural network algorithms, and to solve the problem of insufficient modeling caused by missing network node labels. Firstly, referring to the input layer of GCN, the structuralinformation and node attributes of the network were fused. Secondly, matrix factorization is used to replace the hidden layer of GCN and simulate forward propagation. Then the ideas of identity mapping and vector optimization toachieve vector transformation and model optimization to obtain the network node representation vector, which wereused to simulate the back propagation of GCN. Finally, the similarity matrix for performance evaluation of link prediction tasks was calculated. On the Citeseer dataset, DBLP dataset and Cora dataset, the experimental resultsshowed that ALIP algorithm had a maximum AUC value of 98. 01%, and its performance was superior to the other23 link prediction algorithms. The effectiveness and feasibility of the algorithm, in this study provide a new solutionfor complex unlabeled network link prediction tasks.

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