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.

中文翻译：

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弥合图和网络之间的差距

网络科学已成为描述现实世界复杂的物理、生物、社会和技术系统的结构和动态的强大工具。很大程度上建立在经验观察的基础上，以解决交互的异构、时间和自适应模式，其直观和灵活的性质促成了该领域的普及。随着随机图演化的开创性工作，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，这两个研究社区仍然在很大程度上脱节。在这篇评论中，我们讨论了领域之间进一步交叉授粉的必要性——弥合图和网络之间的差距——以及网络科学如何从这种影响中受益。更数学化的网络科学可能会阐明随机性在建模中的作用，