论文标题
基于内存的消息传递：将传播的消息解耦与歧视
Memory-based Message Passing: Decoupling the Message for Propogation from Discrimination
论文作者
论文摘要
消息传递是图表学习领域中图神经网络的基本过程。基于同义假设，当前消息传递始终汇总连接节点的特征，例如图形拉普拉斯平滑过程。但是，现实世界的图往往是嘈杂和/或不平滑的。同义假设并不总是成立，导致了亚最佳结果。经过修订的消息传递方法需要在汇总邻居的消息时维护每个节点的判别能力。为此，我们提出了一个基于内存的消息传递（MMP）方法，以将每个节点的消息分解为一个自我安装零件，以进行歧视和传播的内存零件。此外，我们开发了一种控制机制和一个解耦正则化，以控制每个节点中的吸收和排除消息的比例。更重要的是，我们的MMP是一项通用技能，可以作为额外的一层来帮助提高传统GNNS性能。在不同同义比率的各种数据集上进行的广泛实验证明了该方法的有效性和鲁棒性。
Message passing is a fundamental procedure for graph neural networks in the field of graph representation learning. Based on the homophily assumption, the current message passing always aggregates features of connected nodes, such as the graph Laplacian smoothing process. However, real-world graphs tend to be noisy and/or non-smooth. The homophily assumption does not always hold, leading to sub-optimal results. A revised message passing method needs to maintain each node's discriminative ability when aggregating the message from neighbors. To this end, we propose a Memory-based Message Passing (MMP) method to decouple the message of each node into a self-embedding part for discrimination and a memory part for propagation. Furthermore, we develop a control mechanism and a decoupling regularization to control the ratio of absorbing and excluding the message in the memory for each node. More importantly, our MMP is a general skill that can work as an additional layer to help improve traditional GNNs performance. Extensive experiments on various datasets with different homophily ratios demonstrate the effectiveness and robustness of the proposed method.
