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.

使用基于图卷积网络（GCN）的半监督图分析方法已获得成功。但是，GCN忽略了图形中每个节点上的一些局部信息，因此数据预处理不完整，并且生成的模型不够准确。因此，在基于图嵌入技术的众多非监督模型的情况下，本地节点信息非常重要。在本文中，我们将基于相似邻居假设的局部分析方法应用于GCN，并提出了局部密度定义。我们称此方法为LDGCN。LDGCN算法通过两种方法处理GCN的输入数据，即不平衡方法和平衡方法。因此，优化的输入数据包含详细的本地节点信息，然后在训练后生成的模型是准确的。我们还通过GCN原理介绍了LDGCN算法的实现，并使用三个主流数据集来验证LDGCN算法的有效性（即Cora，Citeseer和Pubmed数据集）。最后，我们比较了几种主流图分析算法和LDGCN算法的性能。实验结果表明，LDGCN算法在节点分类任务中具有更好的性能。