论文标题
图形神经网络的信息增益比对图结构进行多塑形表征
Multi-duplicated Characterization of Graph Structures using Information Gain Ratio for Graph Neural Networks
论文作者
论文摘要
已经提出了各种图形神经网络（GNN）来解决图形数据的机器学习中的节点分类任务。 GNN通过汇总相邻节点的特征来使用图数据的结构信息。但是，它们无法直接表征和利用结构信息。在本文中，我们建议使用GNNS（MSI-GNN）的信息增益比（IGR）对图结构进行多复制表征，从而通过使用i-Hop邻接矩阵作为图数据的结构信息来增强节点分类的性能。在MSI-GNN中，I-HOP邻接矩阵通过两种方法自适应地调节：（i）基于IGR中矩阵中的结构特征，并且（ii）每个节点中（i）中所选特征是重复的，并且可以灵活地组合。在一个实验中，我们表明我们的MSI-GNN在基准图数据集中的平均准确性方面优于GCN，H2GCN和GCNII。
Various graph neural networks (GNNs) have been proposed to solve node classification tasks in machine learning for graph data. GNNs use the structural information of graph data by aggregating the features of neighboring nodes. However, they fail to directly characterize and leverage the structural information. In this paper, we propose multi-duplicated characterization of graph structures using information gain ratio (IGR) for GNNs (MSI-GNN), which enhances the performance of node classification by using an i-hop adjacency matrix as the structural information of the graph data. In MSI-GNN, the i-hop adjacency matrix is adaptively adjusted by two methods: (i) structural features in the matrix are selected based on the IGR, and (ii) the selected features in (i) for each node are duplicated and combined flexibly. In an experiment, we show that our MSI-GNN outperforms GCN, H2GCN, and GCNII in terms of average accuracies in benchmark graph datasets.
