节点分类任务是一种算法,其必须通过查看其邻居的标签来确定样本(表示为节点)的标签。

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图神经网络在许多基于图的任务中得到了广泛的应用,如节点分类、链路预测和节点聚类。GNNs的性能优势主要来自于对图的边缘执行特征传播和平滑,因此需要足够的连接性和标签信息来进行有效传播。不幸的是,许多现实世界的网络在边缘和标签方面都是稀疏的,这导致了GNN的次优性能。最近对这个稀疏问题的兴趣集中在自训练方法上,它用伪标签扩展监督信号。然而,由于伪标签的质量和数量都不理想,自训练方法本身并不能充分发挥提炼稀疏图学习性能的潜力。在本文中,我们提出了ROD,一种新的接收感知的在线知识提取方法用于稀疏图学习。我们为ROD设计了三种监督信号:多尺度接收感知的图知识、基于任务的监督和丰富的提炼知识,允许知识以同行教学的方式在线迁移。为了提取隐藏在多尺度接收领域中的知识,ROD明确要求个体学生模型保持不同层次的位置信息。对于给定的任务,每个学生根据自己的接受量表知识进行预测,同时结合多尺度知识动态地建立一个强大的教师。我们的方法已经在9个数据集和各种基于图的任务上进行了广泛的评估,包括节点分类、链接预测和节点聚类。结果表明,ROD算法达到了最先进的性能,对图稀疏性具有更强的鲁棒性。

https://www.zhuanzhi.ai/paper/ff1be0c70de3f486fcb3bc2166e469e9

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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}.

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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}.

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