A local density optimization method based on a graph convolutional network

2020, Volume 21, Issue 12

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Frontiers of Information Technology & Electronic Engineering >> 2020, Volume 21, Issue 12 doi: 10.1631/FITEE.1900663

Affiliation(s): College of Computer Science and Technology, Jilin University, Changchun 130012, China; MOE Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun 130012, China; College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130012, China; less

Received: 2019-11-30 Accepted: 2020-12-10 Available online: 2020-12-10

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Abstract

Success has been obtained using a semi-supervised graph analysis method based on a (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 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 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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