Over the past few years, graph representation learning (GRL) has received widespread attention on the feature representations of the non-Euclidean data. As a typical model of GRL, graph convolutional networks (GCN) fuse the graph Laplacian-based static sample structural information. GCN thus generalizes convolutional neural networks to acquire the sample representations with the variously high-order structures. However, most of existing GCN-based variants depend on the static data structural relationships. It will result in the extracted data features lacking of representativeness during the convolution process. To solve this problem, dynamic graph learning convolutional networks (DGLCN) on the application of semi-supervised classification are proposed. First, we introduce a definition of dynamic spectral graph convolution operation. It constantly optimizes the high-order structural relationships between data points according to the loss values of the loss function, and then fits the local geometry information of data exactly. After optimizing our proposed definition with the one-order Chebyshev polynomial, we can obtain a single-layer convolution rule of DGLCN. Due to the fusion of the optimized structural information in the learning process, multi-layer DGLCN can extract richer sample features to improve classification performance. Substantial experiments are conducted on citation network datasets to prove the effectiveness of DGLCN. Experiment results demonstrate that the proposed DGLCN obtains a superior classification performance compared to several existing semi-supervised classification models.

中文翻译：

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用于半监督分类的动态图学习卷积网络

在过去的几年里，图表示学习（GRL）在非欧几里得数据的特征表示上受到了广泛关注。作为 GRL 的典型模型，图卷积网络（GCN）融合了基于图拉普拉斯算子的静态样本结构信息。因此，GCN 泛化了卷积神经网络以获取具有各种高阶结构的样本表示。然而，大多数现有的基于 GCN 的变体都依赖于静态数据结构关系。这将导致在卷积过程中提取的数据特征缺乏代表性。为了解决这个问题，提出了动态图学习卷积网络（DGLCN）在半监督分类上的应用。首先，我们介绍动态谱图卷积操作的定义。它根据损失函数的损失值不断优化数据点之间的高阶结构关系，进而精确拟合数据的局部几何信息。在用一阶切比雪夫多项式优化我们提出的定义后，我们可以获得 DGLCN 的单层卷积规则。由于在学习过程中融合了优化后的结构信息，多层DGLCN可以提取更丰富的样本特征来提高分类性能。在引文网络数据集上进行了大量实验，以证明 DGLCN 的有效性。实验结果表明，与现有的几种半监督分类模型相比，所提出的 DGLCN 获得了优越的分类性能。