基于小图-轨道转换的时间网络比较
Temporal Network Comparison using Graphlet-orbit Transitions
摘要：
网络被广泛用于模拟现实世界的系统，并揭示其拓扑特征。在许多真实世界的网络中，已经计算了网络的度分布和最短路径长度等网络属性，其中大多数都被证明是无标度和小世界网络。小图和网络基序是子图模式，它们捕获的结构信息比前面提到的全局网络属性更丰富，这些局部特征通常用于网络比较。然而，过去关于小图和网络图案的工作几乎只适用于静态网络。许多系统更好地表示为时间网络，它不仅描述了系统在给定阶段的状态，还描述了它们是如何演化的。时间相关的信息在时间网络中至关重要，如果忽略这些数据，静态方法无法获得最佳结果。本文介绍了一种扩展的时间网络的graphlets。我们提出的方法枚举每个网络快照中的所有4节点图轨道，在这个过程中建立相应的轨道转移矩阵。我们的假设是，网络代表类似的系统有特征的轨道转换，更好地识别它们比简单的静态模式，这是评估一组真正的时间网络分为类别。为了进行时间网络比较，我们提出了一个轨道转移协议度量（OTA）。OTA正确地将一组静态网络图案和小图都不能充分地这样做的时间网络分组。此外，我们的方法产生可解释的结果，我们用它来揭示特征轨道转换，这可以被视为一个网络指纹。
英文摘要：
Networks are widely used to model real-world systems and uncover their topological features. Network properties such as the degree distribution and shortest path length have been computed in numerous real-world networks, and most of them have been shown to be both scale-free and small-world networks. Graphlets and network motifs are subgraph patterns that capture richer structural information than aforementioned global network properties, and these local features are often used for network comparison. However, past work on graphlets and network motifs is almost exclusively applicable only for static networks. Many systems are better represented as temporal networks which depict not only how a system was at a given stage but also how they evolved. Time-dependent information is crucial in temporal networks and, by disregarding that data, static methods can not achieve the best possible results. This paper introduces an extension of graphlets for temporal networks. Our proposed method enumerates all 4-node graphlet-orbits in each network-snapshot, building the corresponding orbit-transition matrix in the process. Our hypothesis is that networks representing similar systems have characteristic orbit transitions which better identify them than simple static patterns, and this is assessed on a set of real temporal networks split into categories. In order to perform temporal network comparison we put forward an orbit-transition-agreement metric (OTA). OTA correctly groups a set of temporal networks that both static network motifs and graphlets fail to do so adequately. Furthermore, our method produces interpretable results which we use to uncover characteristic orbit transitions, and that can be regarded as a network-fingerprint.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
