网络比较与系综内图距离
Network comparison and the within-ensemble graph distance
摘要：
量化网络之间的差异是网络科学中一个具有挑战性且始终存在的问题。近年来，针对这一问题提出了多种多样的特别解决办法。在这里，我们提出，简单而易于理解的随机网络集合（如Erdens-Rényi图、随机几何图、Watts-Strogatz图、配置模型和偏好连接网络）是网络比较方法的自然基准。此外，我们还证明了从生成模型中独立采样的两个网络之间的预期距离是一个有用的属性，它包含了该模型的许多关键特征。为了说明我们的结果，我们使用20个通常用于比较图形的距离度量来计算经典网络模型（及其几个参数化）的集合内图形距离和相关量。内集成图距离为图距离的开发人员提供了一个新的框架，以更好地理解他们的创作，并为从业者更好地选择适合他们特定任务的工具。
英文摘要：
Quantifying the differences between networks is a challenging and ever-present problem in network science. In recent years a multitude of diverse, ad hoc solutions to this problem have been introduced. Here we propose that simple and well-understood ensembles of random networks (such as Erdős-Rényi graphs, random geometric graphs, Watts-Strogatz graphs, the configuration model, and preferential attachment networks) are natural benchmarks for network comparison methods. Moreover, we show that the expected distance between two networks independently sampled from a generative model is a useful property that encapsulates many key features of that model. To illustrate our results, we calculate this within-ensemble graph distance and related quantities for classic network models (and several parameterizations thereof) using 20 distance measures commonly used to compare graphs. The within-ensemble graph distance provides a new framework for developers of graph distances to better understand their creations and for practitioners to better choose an appropriate tool for their particular task.
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。
