Enhanced Unsupervised Graph Embedding via Hierarchical Graph Convolution Network

Zhang H.; Zhou J. J.; Li R.

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Enhanced Unsupervised Graph Embedding via Hierarchical Graph Convolution Network

机译：Enhanced Unsupervised Graph Embedding via Hierarchical Graph Convolution Network

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Graph embedding aims to learn the low-dimensional representation of nodes in the network, which has been paid more and more attention in many graph-based tasks recently. Graph Convolution Network (GCN) is a typical deep semisupervised graph embedding model, which can acquire node representation from the complex network. However, GCN usually needs to use a lot of labeled data and additional expressive features in the graph embedding learning process, so the model cannot be effectively applied to undirected graphs with only network structure information. In this paper, we propose a novel unsupervised graph embedding method via hierarchical graph convolution network (HGCN). Firstly, HGCN builds the initial node embedding and pseudo-labels for the undirected graphs, and then further uses GCNs to learn the node embedding and update labels, finally combines HGCN output representation with the initial embedding to get the graph embedding. Furthermore, we improve the model to match the different undirected networks according to the number of network node label types. Comprehensive experiments demonstrate that our proposed HGCN and HGCN*can significantly enhance the performance of the node classification task.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2020年第26期|5702519.1-5702519.9|共9页
作者
Zhang H.; Zhou J. J.; Li R.;
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Shanxi Univ, Sch Comp & Informat Technol, Taiyuan 030006, Peoples R China;

Shanxi Univ, Sch Comp & Informat Technol, Taiyuan 030006, Peoples R China|Shanxi Univ, Minist Educ, Key Lab Computat Intelligence & Chinese Informat, Taiyuan 030006, Peoples R China;

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Enhanced Unsupervised Graph Embedding via Hierarchical Graph Convolution Network

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