A local density optimization method based on a graph convolutional network

Hao WANG; Li-yan DONG; Tie-hu FAN; Ming-hui SUN

doi:10.1631/FITEE.1900663

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A local density optimization method based on a graph convolutional network

Regular Papers | Updated：2022-05-19

- A local density optimization method based on a graph convolutional network
- 一个基于图卷积神经网络的局部密度优化方法
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 12, Pages: 1795-1803(2020)
- Affiliations：
  
  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
- Author bio：
  
  [ "Hao WANG, E-mail: wanghao18@mails.jlu.edu.cn" ]
  [ "Li-yan DONG, E-mail:dongly@jlu.edu.cn" ]
  Tie-hu FAN, E-mail:fth@jlu.edu.cn
  [ "Ming-hui SUN, E-mail:smh@jlu.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1900663
  CLC： TP391
- Received：30 November 2019，
  
  Revised：2020-;9-24，
  
  Published：2020-12
- Accepted：
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Hao WANG, Li-yan DONG, Tie-hu FAN, et al. A local density optimization method based on a graph convolutional network[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1795-1803.
DOI：

Hao WANG, Li-yan DONG, Tie-hu FAN, et al. A local density optimization method based on a graph convolutional network[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1795-1803. DOI： 10.1631/FITEE.1900663.

摘要

基于图卷积神经网络的半监督图分析（GCN）方法已取得成功。然而，该方法忽略了图中节点的某些局部信息，说明GCN数据预处理不够完善，训练产生的模型不够精确。因此，在基于图嵌入技术的多个非监督方法中，对输入数据局部信息的采集非常重要。本文将基于相似邻接度假设的局部分析方法应用到图卷积网络，并给出局部密度的定义；该方法被称作LDGCN。LDGCN通过两种不同方法处理图卷积网络的输入数据，即非平衡方法和平衡方法。被处理后的输入数据包含更详细的结点局部信息，训练所生成的模型更准确。通过GCN原理介绍LDGCN的实现，然后使用3个主流图数据集（Cora，Citeseer和Pubmed）验证其有效性。最后，通过节点分类实验与多个主流图分析方法对比，结果表明LDGCN算法有更好表现。

Abstract

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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