Recently, graph neural networks (GNNs) have become an important and active research direction in deep learning. It is worth noting that most of the existing GNN-based methods learn graph representations within the Euclidean vector space. Beyond the Euclidean space, learning representation and embeddings in hyper-complex space have also shown to be a promising and effective approach. To this end, we propose Quaternion Graph Neural Networks (QGNN) to learn graph representations within the Quaternion space. As demonstrated, the Quaternion space, a hyper-complex vector space, provides highly meaningful computations and analogical calculus through Hamilton product compared to the Euclidean and complex vector spaces. Our QGNN obtains state-of-the-art results on a range of benchmark datasets for graph classification and node classification. Besides, regarding knowledge graphs, our QGNN-based embedding model achieves state-of-the-art results on three new and challenging benchmark datasets for knowledge graph completion. Our code is available at: \url{https://github.com/daiquocnguyen/QGNN}.
翻译:最近,图形神经网络(GNNs)已成为深层学习的一个重要和积极的研究方向,值得指出的是,基于GNN的多数现有方法在厄几里德矢量空间内学习图示,除了欧几里德空间外,学习代表性和嵌入超复杂空间也证明是一种有希望和有效的方法。为此,我们提议Quarternion图神经网络(QGNN)在夸特尔空间内学习图形表达方式。正如所显示的那样,夸特罗空间(一个超复合矢量空间)通过汉密尔顿产品提供与爱几里德和复杂矢量空间相比非常有意义的计算和模拟计算。我们的QNNNN获得了用于图表分类和节点分类的一系列基准数据集的最新结果。此外,关于知识图表,我们基于QGNNN的嵌入模型在三个新的和具有挑战性的基准数据集上取得了最新的结果,用于完成知识图表。我们的代码可以查到:\urlgy/QQong/Qongs.