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CLC number: TP391

On-line Access: 2020-12-10

Received: 2019-11-30

Revision Accepted: 2020-04-21

Crosschecked: 2020-09-24

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Hao Wang


Tie-hu Fan


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.12 P.1795-1803


A local density optimization method based on a graph convolutional network

Author(s):  Hao Wang, Li-yan Dong, Tie-hu Fan, Ming-hui Sun

Affiliation(s):  College of Computer Science and Technology, Jilin University, Changchun 130012, China; more

Corresponding email(s):   wanghao18@mails.jlu.edu.cn, dongly@jlu.edu.cn, fth@jlu.edu.cn, smh@jlu.edu.cn

Key Words:  Semi-supervised learning, Graph convolutional network, Graph embedding, Local density

Hao Wang, Li-yan Dong, Tie-hu Fan, Ming-hui Sun. A local density optimization method based on a graph convolutional network[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1795-1803.

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%DOI 10.1631/FITEE.1900663

T1 - A local density optimization method based on a graph convolutional network
A1 - Hao Wang
A1 - Li-yan Dong
A1 - Tie-hu Fan
A1 - Ming-hui Sun
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 12
SP - 1795
EP - 1803
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900663

Success has been obtained using a semi-supervised graph analysis method based on a graph convolutional network (GCN). However, GCN ignores some local information at each node in the graph, so that data preprocessing is incomplete and the model generated is not accurate enough. Thus, in the case of numerous unsupervised models based on graph embedding technology, local node information is important. In this paper, we apply a local analysis method based on the similar neighbor hypothesis to a GCN, and propose a local density definition; we call this method LDGCN. The LDGCN algorithm processes the input data of GCN in two methods, i.e., the unbalanced and balanced methods. Thus, the optimized input data contains detailed local node information, and then the model generated is accurate after training. We also introduce the implementation of the LDGCN algorithm through the principle of GCN, and use three mainstream datasets to verify the effectiveness of the LDGCN algorithm (i.e., the Cora, Citeseer, and Pubmed datasets). Finally, we compare the performances of several mainstream graph analysis algorithms with that of the LDGCN algorithm. Experimental results show that the LDGCN algorithm has better performance in node classification tasks.





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