Abstract: Multi-Hop Graph Convolutional Network (Multi-Hop GCN) has achieved certain results in alleviating the over-compression problem. However, the multi-hop propagation design has specific parametric information compression loss during the information aggregation process and is sensitive to the local topological structure, which makes it difficult for this type of model to achieve an ideal prediction effect when performing node classification tasks. To address the above problems, this paper starts from the intra-layer and inter-layer perspectives of the multi-hop graph convolutional model, and uses a decoupling-based technique inspired by predictive propagation decoupling and a knowledge jump module to solve the above issues, thereby constructing a new type of multi-hop graph convolutional network—the Knowledge-Semi-Decoupled Multi-Hop Network DrJK-Net. Firstly, a semi-decoupling technique that retains the activation function is proposed to simplify the intra-layer structure of the multi-hop propagation layer. By removing the linear layer in the hidden layer, the number of feature changes during the multi-hop propagation process is reduced, and the parametric information compression loss is decreased. Then, a knowledge jump connection is added between the propagation layers. By connecting all hidden layer embeddings, the model's adaptive selection ability of hidden layer embeddings is improved, and the sensitivity to the local topological structure is reduced. Subsequently, the multi-hop graph convolutional skeleton is combined with the semi-decoupling technique for simplifying intra-layer information propagation and the knowledge jump connection module for establishing inter-layer information channels, proposing a model framework DrJK-Net with lower parametric information compression loss and stronger adaptability to the local topological structure. Finally, comparative experiments and ablation experiments are carried out on multiple public paper networks such as Citeseer, CoraFull, and Actor, as well as social network datasets. The results of the comparative experiments show that DrJK-Net surpasses most cutting-edge models in node classification accuracy and has a significant advantage in running speed. The results of the ablation experiments further verify the effectiveness of the proposed semi-decoupling technique and the introduced knowledge jump connection mechanism, providing new ideas and methods for the development of multi-hop graph convolutional networks.

摘要： 多跳图卷积网络（Multi-Hop GCN）在缓解过压缩问题上具有一定成效，然而多跳传播设计在信息聚合过程中存在一定的参数化信息压缩损失以及对局部拓扑结构敏感，导致该类模型进行节点分类任务时难以达到理想的预测效果。针对上述问题，本文从多跳图卷积模型的层内与层间两个角度出发，采用基于预测传播解耦的解耦式技术和知识跳跃模块对上述问题进行优化，从而构建一种新型多跳图卷积网络——知识-半解耦式多跳网络DrJK-Net。首先，提出一种保留激活函数的半解耦式技术简化多跳传播层内结构，通过去除隐藏层中的线性层，减少多跳传播过程中特征变化次数，降低参数化的信息压缩损失；然后，在传播层间添加知识跳跃连接，通过连接所有隐藏层嵌入，提高模型对隐藏层嵌入的自适应选择能力，降低对局部拓扑结构的敏感度；紧接着，将多跳图卷积骨架与简化层内信息传播的半解耦式技术、建立层间信息通道的知识跳跃连接模块结合，提出参数化信息压损损失更低、对局部拓扑结构适应性更强的模型框架DrJK-Net。最后，在Citeseer、CoraFull与Actor等多个公开论文网络以及社交网络数据集上进行了对比实验与消融实验，对比实验结果表明DrJK-Net在节点分类准确性上超过多数前沿模型且运行速度优势明显，而消融实验结果进一步验证了提出的半解耦式技术与引入的知识跳跃连接机制的有效性，为多跳图卷积网络的发展提供了新的思路与方法。