Message passing (MP) is crucial for effective graph neural networks (GNNs). Most local message-passing schemes have been shown to underperform on heterophily graphs due to the perturbation of updated representations caused by local redundant heterophily information. However, our experiment findings indicate that the distribution of heterophily information during MP can be disrupted by disentangling local neighborhoods. This finding can be applied to other GNNs, improving their performance on heterophily graphs in a more flexible manner compared to most heterophily GNNs with complex designs. This article proposes a new type of simple message-passing neural network called Flow2GNN. It uses a two-way flow message-passing scheme to enhance the ability of GNNs by disentangling and redistributing heterophily information in the topology space and the attribute space. Our proposed message-passing scheme consists of two steps in topology space and attribute space. First, we introduce a new disentangled operator with binary elements that disentangle topology information in-flow and out-flow between connected nodes. Second, we use an adaptive aggregation model that adjusts the flow amount between homophily and heterophily attribute information. Furthermore, we rigorously prove that disentangling in message-passing can reduce the generalization gap, offering a deeper understanding of how our model enhances other GNNs. The extensive experiment results show that the proposed model, Flow2GNN, not only outperforms state-of-the-art GNNs, but also helps improve the performance of other commonly used GNNs on heterophily graphs, including GCN, GAT, GCNII, and H 2_{2} GCN, specifically for GCN, with up to a 25.88% improvement on the Wisconsin dataset.

中文翻译：

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Flow2GNN：灵活的双向流消息传递，用于增强 GNN 超越同质性


消息传递 （MP） 对于有效的图形神经网络 （GNN） 至关重要。由于本地冗余异质信息引起的更新表示的扰动，大多数本地消息传递方案已被证明在异质图上表现不佳。然而，我们的实验结果表明，解开局部邻域可以破坏 MP 期间异质信息的分发。这一发现可以应用于其他 GNN，与大多数具有复杂设计的异质 GNN 相比，以更灵活的方式提高它们在异质图上的性能。本文提出了一种新型的简单消息传递神经网络，称为 Flow2GNN。它使用双向流消息传递方案，通过在拓扑空间和属性空间中解开和重新分配异质信息来增强 GNN 的能力。我们提出的消息传递方案包括拓扑空间和属性空间的两个步骤。首先，我们引入了一个新的解纠运算符，其中包含二进制元素，用于解开连接节点之间的拓扑信息流入和流出。其次，我们使用自适应聚合模型来调整同质和异质属性信息之间的流量。此外，我们严格证明，解开消息传递可以减少泛化差距，从而更深入地了解我们的模型如何增强其他 GNN。广泛的实验结果表明，所提出的模型 Flow2GNN 不仅优于最先进的 GNN，而且还有助于提高其他常用 GNN 在异质图上的性能，包括 GCN、GAT、GCNII 和 H 2_{2} GCN，特别是对于 GCN，比威斯康星数据集提高了 25.88%。