Generating realistic scaled complex networks
Generating realistic scaled complex networks
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DOI：
10.1007/s41109-017-0054-z
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发表时间：
2016-09
影响因子：
2.2
通讯作者：
Christian Staudt;M. Hamann;Alexander Gutfraind;Ilya Safro;Henning Meyerhenke
中科院分区：
文献类型：
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作者：
Christian Staudt;M. Hamann;Alexander Gutfraind;Ilya Safro;Henning Meyerhenke
生成模型的研究在新兴的网络科学领域中起着核心作用，研究如何通过形式规则生成真实的网络中的统计模式。这些生成模型的输出是设计和评估网络计算方法的基础，包括验证和模拟研究。在过去的二十年中，各种各样的模型已经提出了一个最终目标，实现全面的现实主义所产生的网络。在这项研究中，我们（a）介绍了一种新的生成器，称为ReCoN;（B）探索如何ReCoN和一些现有的模型可以拟合到一个原始的网络，以产生一个结构相似的副本，（c）使用ReCoN产生网络比原来的样本大得多，最后（d）讨论开放的问题和有前途的研究方向。在比较实验研究中，我们发现ReCoN通常上级许多其他最先进的网络生成方法。我们认为，ReCoN是一个可扩展的和有效的工具，用于建模一个给定的网络，同时保留在微观和宏观尺度上的重要属性，并按数量级的大小缩放样本数据。
Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks including verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.