期刊论文

Mugang Lin

英文

Entropy

1099-4300

2023

25

4

567-

10.3390/e25040567

This research was supported in part by the Scientific Research Fund of Hunan Provincial Education Department (22A0502), the National Natural Science Foundation of China (61772179), the Hunan Provincial Natural Science Foundation of China (2019JJ40005), the 14th Five-Year Plan Key Disciplines and Application-Oriented Special Disciplines of Hunan Province (Xiangjiaotong (2022) 351), the Science and Technology Plan Project of Hunan Province (2016TP1020), the Science and Technology Innovation Project of Hengyang (202250045231), the Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University (2022HSKFJJ012), and the Postgraduate Scientific Research Innovation Project of Hunan Province (QL20210262).

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The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and th...