微分网络分析与图分类：一种全局局部方法
Differential network analysis and graph classification: a glocal approach
摘要：
基于全局HIM度量及其导出的图核，我们提出了一种新的差分网络分析解决方案，集成了网络比较和分类任务。HIM距离被定义为线性组合归一化汉明距离H和归一化Ipsen-Mikhailov谱距离IM的乘积度量的单参数族。在一个单一的度量中的两个组件的组合允许克服它们的缺点，并获得同时是全球和本地的措施。此外，将HIM核嵌入到支持向量机中，给出了一种基于HIM距离的分类算法。首先，我们概述了度量结构的理论基础。我们介绍了两个不同的应用程序的HIM距离和HIM内核的生物数据集。这种多功能性支持采用HIM家族作为信息提取的通用工具，量化复杂系统的不同物质之间的差异。HIM指标的开源实现由R包nettols及其Web接口ReNette提供。
英文摘要：
Based on the glocal HIM metric and its induced graph kernel, we propose a novel solution in differential network analysis that integrates network comparison and classification tasks. The HIM distance is defined as the one-parameter family of product metrics linearly combining the normalised Hamming distance H and the normalised Ipsen-Mikhailov spectral distance IM. The combination of the two components within a single metric allows overcoming their drawbacks and obtaining a measure that is simultaneously global and local. Furthermore, plugging the HIM kernel into a Support Vector Machine gives us a classification algorithm based on the HIM distance. First, we outline the theory underlying the metric construction. We introduce two diverse applications of the HIM distance and the HIM kernel to biological datasets. This versatility supports the adoption of the HIM family as a general tool for information extraction, quantifying difference among diverse in- stances of a complex system. An Open Source implementation of the HIM metrics is provided by the R package nettols and in its web interface ReNette.
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