具有时间相关零模型的随机增长网络
Randomizing growing networks with a time-respecting null model
摘要：
复杂网络通常被用来表示不是静态的，而是随着时间而增长的系统：人们建立新的友谊，发表新的论文并引用现有的论文，等等。为了评估在这种网络上进行的测量的统计意义，我们提出了一种随机化方法-一种尊重时间的空模型-它既保留了网络的度序列，又保留了各个节点度值的时间演化。通过保留所分析的系统的时间连接模式，该模型是能够因素出系统的时间模式对它的结构的影响。我们将该模型应用于《物理评论》学术论文的引用网络和美国电影的引用网络。该模型表明，这两个数据集是显着不同的程度的相关性，我们讨论了这一发现的重要影响提供的信息的范例节点中心性指标，如indegree和谷歌的PageRank。这里提出的随机化方法可以用来评估增长网络中任何结构属性的重要性，这可以为空模型发挥关键作用的问题带来新的见解，例如社区和网络图案的检测。
英文摘要：
Complex networks are often used to represent systems that are not static but grow with time: people make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology---a time-respecting null model---that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two datasets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.
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