标题： 通过单节点模式的自动网络指纹识别 显示英文标题

标题： Automatic Network Fingerprinting through Single-Node Motifs

摘要： 复杂网络以其特定的连接模式（网络基序）为特征，但它们的构建块也可以通过节点基序——局部网络特征的组合来识别和描述。 Costa 等人提出了一种识别单个节点基序的技术。 （L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, 和 M. Kaiser, Europhys. Lett., 87, 1, 2009）。 在这里，我们首先建议对该方法进行改进，包括如何自动确定其参数。 这样的自动流程使得对许多网络进行高通量研究成为可能。 其次，新的流程在不同的网络系列中进行了验证。 第三，我们提供了一个例子，说明该方法可以用于分析网络时间序列。 总之，我们提供了一种稳健的方法，用于系统地发现和分类网络的特征节点。 与传统的基序分析不同，我们的方法可以识别特定于网络的个体组件（此处：节点）。 这些特殊节点，如枢纽节点，可能在现实世界网络中发挥关键作用。 显示英文摘要

摘要： Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.