graph convolutional matrix completion

图卷积矩阵补全是一种基于图卷积神经网络的矩阵补全方法，用于预测缺失的矩阵元素。它将矩阵看作一个图，将矩阵中的每个元素作为节点，利用图卷积神经网络学习节点的嵌入表示，从而预测缺失的元素。该方法在推荐系统、社交网络分析等领域有广泛应用。

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Dual graph convolutional network

Dual Graph Convolutional Network (DGCN) is a neural network architecture designed for graph-based learning tasks. In traditional graph convolutional networks, the convolution operation is defined on the node feature matrix. However, DGCN extends this concept to the graph dual structure, which is a new graph constructed by swapping nodes and edges in the original graph.

In DGCN, the convolution operation is performed on the dual graph structure and captures the local structural information of the original graph. This allows DGCN to capture both the node-level and edge-level features of the graph, leading to improved performance on tasks such as node classification and link prediction.

DGCN has been applied successfully in various domains such as social network analysis, recommendation systems, and drug discovery.

graph convolutional network

图卷积网络（Graph Convolutional Network）是一种基于图结构数据的深度学习模型，它可以对节点和边进行特征提取和表示学习，从而实现图分类、节点分类、链接预测等任务。该模型的核心思想是将卷积操作推广到图结构上，通过局部连接和权值共享来提取节点的特征表示。图卷积网络已经在社交网络分析、化学分子结构预测、推荐系统等领域取得了很好的效果。