Attributed Graph Embedding Based on Attention with Cluster

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

Mathematics, 2022, vol. 10, issue 23, 1-14

Abstract: Graph embedding is of great significance for the research and analysis of graphs. Graph embedding aims to map nodes in the network to low-dimensional vectors while preserving information in the original graph of nodes. In recent years, the appearance of graph neural networks has significantly improved the accuracy of graph embedding. However, the influence of clusters was not considered in existing graph neural network (GNN)-based methods, so this paper proposes a new method to incorporate the influence of clusters into the generation of graph embedding. We use the attention mechanism to pass the message of the cluster pooled result and integrate the whole process into the graph autoencoder as the third layer of the encoder. The experimental results show that our model has made great improvement over the baseline methods in the node clustering and link prediction tasks, demonstrating that the embeddings generated by our model have excellent expressiveness.

Keywords: network representation learning; attributed graph embedding; graph autoencoder; graph neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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