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A New Method Based on Graph Transformation for FAS Mining in Multi-graph Collections

机译:基于图变换的多图集合FAS挖掘新方法

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Currently, there has been an increase in the use of frequent approximate subgraph (FAS) mining for different applications like graph classification. In graph classification tasks, FAS mining algorithms over graph collections have achieved good results, specially those algorithms that allow distortions between labels, keeping the graph topology. However, there are some applications where multi-graphs are used for data representation, but FAS miners have been designed to work only with simple-graphs. Therefore, in this paper, in order to deal with multi-graph structures, we propose a method based on graph transformations for FAS mining in multi-graph collections.
机译:当前,频繁的近似子图(FAS)挖掘在诸如图分类之类的不同应用中的使用已经增加。在图分类任务中,基于图集合的FAS挖掘算法取得了良好的效果,特别是那些允许标签之间变形,保持图拓扑结构的算法。但是,在某些应用程序中,多图用于数据表示,但是FAS矿工已被设计为仅可用于简单图。因此,在本文中,为了处理多图结构,我们提出了一种基于图变换的多图集合中FAS挖掘方法。

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