论文标题
桥接图形和网络之间的差距
Bridging the gap between graphs and networks
论文作者
论文摘要
网络科学已成为描述现实世界复杂物理，生物学，社会和技术系统的结构和动态的强大工具。它的直观和灵活的性质在很大程度上建立在解决异质，时间和适应性的互动模式上，这有助于该领域的普及。随着随机图的演变的开创性工作，图理论通常被认为是网络科学的数学基础。尽管有这样的叙述，但两个研究社区仍然在很大程度上断开了连接。在本评论中，我们讨论了在田地之间进行进一步交叉授粉的必要性 - 弥合图形和网络之间的差距 - 网络科学如何从这种影响中受益。更具数学网络的科学可以阐明随机性在建模中的作用，暗示了基本的行为定律，并预测了自然界中未观察到但未观察到的复杂网络现象。
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.
