多通道图卷积网络
Multi-Channel Graph Convolutional Networks
摘要：
图神经网络（GNN）已被证明是有效的分类图结构。为了进一步提高图的表示学习能力，层次GNN已被探索。它利用可微分池将节点聚类成固定的组，并生成一个粗粒度的结构伴随着原始图的收缩。然而，这样的聚类会丢弃一些图信息，并达到次优的结果。这是因为节点本身具有不同的特征或角色，两个非同构图可能具有相同的粗粒度结构，在池化后无法区分。为了弥补粗粒度聚类造成的损失，进一步提高GNN，我们提出了一种多通道图卷积网络（MuchGCN）。它是由卷积神经网络，在一系列的通道编码，以保持输入图像的综合特征的动机。因此，我们定义了特定的图卷积来学习每一层的一系列图通道，并迭代地池图来编码分层结构。已经仔细进行了实验，以证明MuchGCN的优越性超过国家的最先进的图分类算法。
英文摘要：
Graph neural networks (GNN) has been demonstrated to be effective in classifying graph structures. To further improve the graph representation learning ability, hierarchical GNN has been explored. It leverages the differentiable pooling to cluster nodes into fixed groups, and generates a coarse-grained structure accompanied with the shrinking of the original graph. However, such clustering would discard some graph information and achieve the suboptimal results. It is because the node inherently has different characteristics or roles, and two non-isomorphic graphs may have the same coarse-grained structure that cannot be distinguished after pooling. To compensate the loss caused by coarse-grained clustering and further advance GNN, we propose a multi-channel graph convolutional networks (MuchGCN). It is motivated by the convolutional neural networks, at which a series of channels are encoded to preserve the comprehensive characteristics of the input image. Thus, we define the specific graph convolutions to learn a series of graph channels at each layer, and pool graphs iteratively to encode the hierarchical structures. Experiments have been carefully carried out to demonstrate the superiority of MuchGCN over the state-of-the-art graph classification algorithms.
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