HP-GMN: Graph Memory Networks for Heterophilous Graphs

Graph neural networks (GNNs) have achieved great success in various graph problems. However, most GNNs are Message Passing Neural Networks (MPNNs) based on the homophily assumption, where nodes with the same label are connected in graphs. Real-world problems bring us heterophily problems, where nodes with different labels are connected in graphs. MPNNs fail to address the heterophily problem because they mix information from different distributions and are not good at capturing global patterns. Therefore, we investigate a novel Graph Memory Networks model on Heterophilous Graphs (HP-GMN) to the heterophily problem in this paper. In HP-GMN, local information and global patterns are learned by local statistics and the memory to facilitate the prediction. We further propose regularization terms to help the memory learn global information. We conduct extensive experiments to show that our method achieves state-of-the-art performance on both homophilous and heterophilous graphs. The code of this paper can be found at https://github.com/junjie-xu/HP-GMN.

图神经网络（GNNs）在各种图问题上取得了巨大成功。然而，大多数图神经网络是基于同质性假设的消息传递神经网络（MPNNs），即在图中相同标签的节点相互连接。现实世界的问题给我们带来了异质性问题，即图中不同标签的节点相互连接。MPNNs无法解决异质性问题，因为它们混合了来自不同分布的信息，并且不擅长捕捉全局模式。因此，在本文中，我们针对异质图上的异质性问题研究了一种新的图记忆网络模型（HP - GMN）。在HP - GMN中，通过局部统计量和记忆来学习局部信息和全局模式以促进预测。我们还进一步提出了正则化项来帮助记忆学习全局信息。我们进行了大量实验，结果表明我们的方法在同质图和异质图上都取得了最先进的性能。本文的代码可在https://github.com/junjie - xu/HP - GMN找到。