用高阶结构关系改进图神经网络消息传递，提升深度与稳定性。
GCN-MPPR: Enhancing the Propagation of Message Passing Neural Networks via Motif-Based Personalized PageRank
- 基于图元模式设计新型PageRank，衡量节点间高阶影响。
- 在多个数据集上准确率超越基线，训练更稳定且耗时更低。
- 可作为通用模块适配各类GCN任务，尤其适合复杂图结构建模。
基于消息传递的图神经网络（MPNN）在各类图应用中取得显著进展，但普遍存在信息传播范围受限、深度不足的问题，主要由过平滑导致。现有方法虽尝试通过优化或结构调整解决，但整体仍面临精度不高、稳定性差和计算开销大的挑战。此外，传统消息传递忽略了高阶图结构关系，进一步制约性能。为此，本文提出一种基于图元模式的个性化PageRank（MPPR），用于衡量节点间的高阶影响。将MPPR引入GCN的消息传递过程，实现更高层次的信息引导。实验表明，该方法在准确率、稳定性和时间开销上均优于多数基线模型。所提方法可作为通用组件集成于各类GCN任务中，实验验证了其在DGCRL中的有效性。匿名代码仓库已公开：https://anonymous.4open.science/r/GCN-MPPR-AFD6/。
原文摘要 · Abstract (English)
The algorithms based on message passing neural networks (MPNNs) on graphs have recently achieved great success for various graph applications. However, studies find that these methods always propagate the information to very limited neighborhoods with shallow depth, particularly due to over-smoothing. That means most of the existing MPNNs fail to be so `deep'. Although some previous work tended to handle this challenge via optimization- or structure-level remedies, the overall performance of GCNs still suffers from limited accuracy, poor stability, and unaffordable computational cost. Moreover, neglect of higher-order relationships during the propagation of MPNNs has further limited the performance of them. To overcome these challenges, a novel variant of PageRank named motif-based personalized PageRank (MPPR) is proposed to measure the influence of one node to another on the basis of considering higher-order motif relationships. Secondly, the MPPR is utilized to the message passing process of GCNs, thereby guiding the message passing process at a relatively `high' level. The experimental results show that the proposed method outperforms almost all of the baselines on accuracy, stability, and time consumption. Additionally, the proposed method can be considered as a component that can underpin almost all GCN tasks, with DGCRL being demonstrated in the experiment. The anonymous code repository is available at: https://anonymous.4open.science/r/GCN-MPPR-AFD6/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。