架起图表和网络之间的桥梁（CS SI）

• 2020 年 4 月 6 日
• 筆記

网络科学已经成为描述现实世界中复杂的物理、生物、社会和技术系统的结构和动态的有力工具。主要建立在经验观察的基础上，以解决异质性，暂时性和适应性的相互作用模式，其直观和灵活的性质有助于该领域的普及。随着对随机图进化的开创性工作，图论经常被引用为网络科学的数学基础。 尽管有这样的叙述，两个研究团体在很大程度上仍然没有联系。在这篇评论中，我们讨论了在领域之间进一步交叉授粉的必要性——架起图形和网络之间的桥梁——以及网络科学如何从这种影响中受益 一个更加数学化的网络科学可能会阐明随机性在建模中的作用，暗示行为的潜在规律，并预测自然界中尚未观测到的复杂的网络现象。

原文题目：Bridging the gap between graphs and networks

原文：Network science has become a powerful tool to describe the structure and dynamics of real-world complex physical, biological, social, and technological systems. Largely built on empirical observations to tackle heterogeneous, temporal, and adaptive patterns of interactions, its intuitive and flexible nature has contributed to the popularity of the field. With pioneering work on the evolution of random graphs, graph theory is often cited as the mathematical foundation of network science. Despite this narrative, the two research communities are still largely disconnected. In this Commentary we discuss the need for further cross-pollination between fields — bridging the gap between graphs and networks — and how network science can benefit from such influence. A more mathematical network science may clarify the role of randomness in modeling, hint at underlying laws of behavior, and predict yet unobserved complex networked phenomena in nature.

原文作者：Gerardo Iñiguez

原文地址：https://arxiv.org/abs/2004.01467