基于图卷积神经网络的自注意力的融合节点分类框架

Fusion Node Classification Framework Based on Self-attention in Graph Convolutional Networks

图神经网络因其强大的建模能力引起广泛关注,常常被用来解决图上的节点分类任务.现阶段常用的以图卷积神经网络(graph convolutional network,GCN)为内核的模型解决此类问题,但往往因为出现过拟合与过平滑而导致深层的节点嵌入表示效果并不好.因此,本文提出了一种基于GCN内核的结合残差连接与自注意力方法——GCNRN模型,以提升GCN的泛化能力.同时,为了整合更深入的信息,本文引入融合机制,采用模糊积分融合多个分类器,最终提高模型测试精度.为了验证所提出方法的优越性,本文采用ogbn-arxiv与常用的引文数据集进行了对比实验.GCNRN模型与多个以GCN为内核的现有模型相比,节点分类准确率平均提高了2%,且避免了传统的过拟合和过平滑现象.此外,实验结果表明,增加了基于模糊积分的融合模块的多分类器模型比传统融合方法具有更好的分类效果.

Graph neural networks(GNNs)have attracted widespread attention due to their powerful modeling capabilities,and they are often used to solve node classification tasks on graphs.At this stage,the commonly used model with the graph convolutional network(GCN)as the core solves such problems.However,due to over-fitting and over-smoothing,the deep node embedding representation effect is not positive.Therefore,this study proposes a graph convolutional neural residual networks(GCNRN)model that combines residual connection and self-attention based on GCN kernel to improve the generalization ability of GCN.At the same time,in order to integrate more in-depth information,this study introduces a fusion mechanism,uses fuzzy integral to fuse multiple classifiers,and finally improves the model testing accuracy.In order to verify the superiority of the proposed method,this study uses ogbn-arxiv and commonly used citation datasets to conduct comparative experiments.Compared with many existing models with GCN as the core,the GCNRN model has an average improvement of node classification accuracy by 2%and avoids the traditional over-fitting and over-smoothing phenomena.In addition,the experimental results show that the multi-classifier model with the fusion module based on fuzzy integral has a better classification effect than the traditional fusion method.