GCN-MPPR: 通过基于图 motif 的个性化 PageRank 增强消息传递神经网络的信息传播
GCN-MPPR: Enhancing the Propagation of Message Passing Neural Networks via Motif-Based Personalized PageRank
AI总结：
GCN-MPPR通过基于motif的个性化PageRank改进信息传播，提升GCN在精度、稳定性和效率上的表现。
摘要：
基于消息传递神经网络（MPNNs）的图算法最近在各种图应用中取得了巨大成功。然而，研究发现这些方法总是将信息传播到非常有限的邻域中，深度较浅，特别是由于过平滑问题。这意味着大多数现有的MPNNs都无法做到如此'深'。尽管一些先前的工作试图通过优化或结构层面的解决方案来应对这一挑战，但GCNs的整体性能仍然受到精度有限、稳定性差和计算成本高昂的限制。此外，在信息传播过程中忽视更高阶的关系进一步限制了它们的性能。为了克服这些挑战，提出了一种新的PageRank变种，称为基于图motif的个性化PageRank（MPPR），用于测量一个节点对另一个节点的影响，基于考虑更高阶的motif关系。其次，MPPR被用于GCNs的消息传递过程中，从而在相对'高'的层面指导信息传递过程。实验结果表明，所提出的方法在准确率、稳定性和时间消耗方面几乎都优于所有基线方法。此外，所提出的方法可以被视为几乎所有GCN任务的基础组件，通过实验演示了DGCRL。匿名代码库可在：https://anonymous.4open.science/r/GCN-MPPR-AFD6/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
