论文标题
半监督学习的各向异性图卷积网络
Anisotropic Graph Convolutional Network for Semi-supervised Learning
论文作者
论文摘要
图形卷积网络学习有效的节点嵌入，这些嵌入方式可在半监督的学习任务（例如节点分类）中实现高准确性预测有用。但是，这些网络遇到了图形过度平滑和收缩效果的问题，这在很大程度上是由于它们使用线性拉普拉斯流动流跨图边缘扩散了特征。对于节点分类的任务，此限制尤其有问题，在该任务中，目标是预测与图节点关联的标签。为了解决此问题，我们通过引入非线性函数，在捕获节点的信息功能的同时，在防止超齿中摄入过度尺寸的同时，提出了一个各向异性图卷积网络，用于半监督节点分类。所提出的框架在很大程度上是由各向异性扩散在图像和几何处理中的良好性能的动机，并根据本地图结构和节点特征学习非线性表示。与标准基线方法相比，在三个引用网络和两个图像数据集中证明了我们方法的有效性，从而实现了更好或可比的分类精度结果。
Graph convolutional networks learn effective node embeddings that have proven to be useful in achieving high-accuracy prediction results in semi-supervised learning tasks, such as node classification. However, these networks suffer from the issue of over-smoothing and shrinking effect of the graph due in large part to the fact that they diffuse features across the edges of the graph using a linear Laplacian flow. This limitation is especially problematic for the task of node classification, where the goal is to predict the label associated with a graph node. To address this issue, we propose an anisotropic graph convolutional network for semi-supervised node classification by introducing a nonlinear function that captures informative features from nodes, while preventing oversmoothing. The proposed framework is largely motivated by the good performance of anisotropic diffusion in image and geometry processing, and learns nonlinear representations based on local graph structure and node features. The effectiveness of our approach is demonstrated on three citation networks and two image datasets, achieving better or comparable classification accuracy results compared to the standard baseline methods.
