Attribute Graph Embedding Based on Multi-Order Adjacency Views and Attention Mechanisms

Jinfang Sheng, Zili Yang, Bin Wang () and Yu Chen
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Jinfang Sheng: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Zili Yang: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Bin Wang: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Yu Chen: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Mathematics, 2024, vol. 12, issue 5, 1-15

Abstract: Graph embedding plays an important role in the analysis and study of typical non-Euclidean data, such as graphs. Graph embedding aims to transform complex graph structures into vector representations for further machine learning or data mining tasks. It helps capture relationships and similarities between nodes, providing better representations for various tasks on graphs. Different orders of neighbors have different impacts on the generation of node embedding vectors. Therefore, this paper proposes a multi-order adjacency view encoder to fuse the feature information of neighbors at different orders. We generate different node views for different orders of neighbor information, consider different orders of neighbor information through different views, and then use attention mechanisms to integrate node embeddings from different views. Finally, we evaluate the effectiveness of our model through downstream tasks on the graph. Experimental results demonstrate that our model achieves improvements in attributed graph clustering and link prediction tasks compared to existing methods, indicating that the generated embedding representations have higher expressiveness.

Keywords: graph embedding; graph representation learning; graph neural networks; graph autoencoder (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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