RA-GCN：抑制过平滑现象的文本分类算法

现有大多数利用图神经网络的算法进行文本分类时，忽略了图神经网络的过度平滑问题和由于文本图拓扑差异引入的误差，导致文本分类的性能不佳。针对这一问题，提出了衡量多个文本图表示的平滑度方法WACD以及抑制过平滑现象的正则项RWACD。随后提出了基于注意力和残差的网络结构ARS，用于弥补由于图拓扑差异引起的文本信息的损失。最后，提出了图卷积神经网络文本分类算法RA-GCN。RA-GCN在图表示学习层使用ARS融合文本表示，在读出层使用RWACD抑制过平滑现象。在六个中英文数据集上进行实验，实验结果证明了RA-GCN的分类性能，并通过多个对比实验验证了RWACD和ARS的作用。

RA-GCN: text classification algorithm suppressing over-smoothing phenomenon

Most existing text classification algorithms based on graph neural network ignore the problem of over-smoothing, and ignore the problem of information loss due to graph topology, resulting in poor classification performance. To solve this problem, this paper proposed a method to measure the smoothness of multiple text graph representations WACD and a regularization term RWACD to suppress over-smoothing. Subsequently, this paper proposed an attention and residual-based network structure ARS to compensate for the loss of textual information due to graph topology differences. Finally, this paper proposed a graph convolutional neural network text classification algorithm RA-GCN. RA-GCN used ARS to fuse text representations in the graph representation learning layer, and used RWACD in the readout layer to suppress over-smoothing. This paper conducted experiments on 6 Chinese and English datasets. The experimental results demonstrate the classification performance of RA-GCN, and the effects of RWACD and ARS are verified through multiple comparative experiments.