Link prediction framework using graph neural network based on subgraph

Articles in Press

Document Type : Original Article

Authors

Electrical and Computer Engineering Department, University of Kashan, Kashan, Iran

10.22052/scj.2024.253458.1179

Abstract

Link prediction is one of the important topics in network analysis. Link prediction can be done by a classifier such that the feature vector of a pair of nodes is its input. The output of the classifier indicates whether a link is predicted between that pair of nodes (class one or class zero). To extract the feature vector of a pair of nodes, neural networks (GNN) can be used, in which case the method of solving the link prediction problem will be based on GNN. In this paper, a GNN-based link prediction problem solving method called GAE is considered as the basic method. One of the basic problems in this method is that the feature vector extracted by GNN can be the same for different pairs of nodes. To solve this problem, in this paper, using the concept of subgraph, the basic method is improved and a new framework called SGAE is proposed. The proposed framework has been compared with the basic methods based on different evaluation criteria, which shows its improved performance. For example, the SGAE method has improved 5.5, 5, 5.75 and 5.87 compared to the basic GAE method in terms of accuracy, F1-Score, average precision and area under the precision-recall curve.

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Main Subjects

Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 26 January 2024

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History

  • Receive Date: 22 August 2023
  • Revise Date: 25 October 2023
  • Accept Date: 23 January 2024

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