论文标题
使用多层相邻合并模型的图形嵌入
Graph embedding using multi-layer adjacent point merging model
论文作者
论文摘要
对于图形分类任务，许多传统的内核方法着重于测量图之间的相似性。这些方法在解决图同构问题方面取得了巨大成功。但是，在某些分类问题中，图类不仅取决于整个图的拓扑相似性，还取决于组成子图模式。为此，我们使用多层相邻合并模型提出了一种新型的图形嵌入方法。这种嵌入方法使我们能够从火车数据数据中提取不同的子图模式。然后，我们为特征选择提出了灵活的损失函数，该功能增强了我们对于不同分类问题的鲁棒性。最后，数值评估表明，我们所提出的方法的表现优于许多最新方法。
For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs. These methods have achieved great success on resolving graph isomorphism problems. However, in some classification problems, the graph class depends on not only the topological similarity of the whole graph, but also constituent subgraph patterns. To this end, we propose a novel graph embedding method using a multi-layer adjacent point merging model. This embedding method allows us to extract different subgraph patterns from train-data. Then we present a flexible loss function for feature selection which enhances the robustness of our method for different classification problems. Finally, numerical evaluations demonstrate that our proposed method outperforms many state-of-the-art methods.
