论文标题
差异描述图的组
Differentially Describing Groups of Graphs
论文作者
论文摘要
自闭症儿童的神经连通性与健康儿童或自闭症青少年的神经连通性有何不同？全球贸易网络中的哪些模式在商品类别之间共享，这些模式会随着时间的流逝而变化？回答这样的问题需要我们差异地描述图组：给定一组图和这些图形分组成组，发现一个组中的图形有什么共同点，它们与其他组中的图形有系统差异，以及多个图的相关组如何相关。我们将此任务称为图形组分析，该任务旨在通过统计学上的显着子图来描述图组之间的相似性和差异。为了执行图形组分析，我们介绍了Gragra，该Gragra使用最大的熵建模来识别与一个或多个图形组具有统计学意义关联的非冗余子图集。通过在各种合成和现实世界图组上进行的一系列实验，我们确认Gragra在实践中效果很好。
How does neural connectivity in autistic children differ from neural connectivity in healthy children or autistic youths? What patterns in global trade networks are shared across classes of goods, and how do these patterns change over time? Answering questions like these requires us to differentially describe groups of graphs: Given a set of graphs and a partition of these graphs into groups, discover what graphs in one group have in common, how they systematically differ from graphs in other groups, and how multiple groups of graphs are related. We refer to this task as graph group analysis, which seeks to describe similarities and differences between graph groups by means of statistically significant subgraphs. To perform graph group analysis, we introduce Gragra, which uses maximum entropy modeling to identify a non-redundant set of subgraphs with statistically significant associations to one or more graph groups. Through an extensive set of experiments on a wide range of synthetic and real-world graph groups, we confirm that Gragra works well in practice.
