利用初始残差和解耦操作的自适应深层图卷积

Adaptive deep graph convolution using initial residual and decoupling operations

传统的图卷积网络(GCN)及其很多变体都是在浅层时达到最佳的效果,而没有充分利用图中节点的高阶邻居信息。随后产生的深层图卷积模型可以解决以上问题却又不可避免地产生了过平滑的问题,导致模型无法有效区分图中不同类别的节点。针对此问题,提出了一种利用初始残差和解耦操作的自适应深层图卷积模型ID-AGCN。首先,对节点的表示转换以及特征传播进行解耦;然后,在节点的特征传播过程中添加了初始残差;最后,自适应地结合不同传播层得到的节点表示,针对每个节点选择其合适的局部信息和全局信息以得到含有丰富信息的节点表征,并利用少部分带标签的节点进行监督训练来生成最终的节点表征。在Cora、CiteSeer和PubMed这三个数据集上的实验结果表明,ID-AGCN的分类准确率相较GCN分别提高了约3.4个百分点、2.3个百分点和1.9个百分点。所提模型能够更好地缓解过平滑。

The traditional Graph Convolutional Network(GCN)and many of its variants achieve the best effect in the shallow layers,and do not make full use of higher-order neighbor information of nodes in the graph.The subsequent deep graph convolution models can solve the above problem,but inevitably generate the problem of over-smoothing,which makes the models impossible to effectively distinguish different types of nodes in the graph.To address this problem,an adaptive deep graph convolution model using initial residual and decoupling operations,named ID-AGCN(model using Initial residual and Decoupled Adaptive Graph Convolutional Network),was proposed.Firstly,the node’s representation transformation as well as feature propagation was decoupled.Then,the initial residual was added to the node’s feature propagation process.Finally,the node representations obtained from different propagation layers were combined adaptively,appropriate local and global information was selected for each node to obtain node representations containing rich information,and a small number of labeled nodes were used for supervised training to generate the final node representations.Experimental result on three datasets Cora,CiteSeer and PubMed indicate that the classification accuracy of ID-AGCN is improved by about 3.4 percentage points,2.3 percentage points and 1.9 percentage points respectively,compared with GCN.The proposed model has superiority in alleviating over-smoothing.