A network is a typical expressive form of representing complex systems in terms of vertices and links, in which the pattern of interactions amongst components of the network is intricate. The network can be static that does not change over time or dynamic that evolves through time. The complication of network analysis is different under the new circumstance of network size explosive increasing. In this paper, we introduce a new network science concept called a big network. A big networks is generally in large-scale with a complicated and higher-order inner structure. This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network. We first introduce the structural characteristics of big networks from three levels, which are micro-level, meso-level, and macro-level. We then discuss some state-of-the-art advanced topics of big network analysis. Big network models and related approaches, including ranking methods, partition approaches, as well as network embedding algorithms are systematically introduced. Some typical applications in big networks are then reviewed, such as community detection, link prediction, recommendation, etc. Moreover, we also pinpoint some critical open issues that need to be investigated further.

网络是在顶点和链接方面表示复杂系统的典型表示形式，其中，网络组件之间的交互模式是复杂的。网络可以是不随时间变化的静态网络，也可以是随时间变化的动态网络。在网络规模爆炸性增长的新情况下，网络分析的复杂性有所不同。在本文中，我们介绍了一种称为大网络的新网络科学概念。大型网络通常是大规模的，具有复杂的高阶内部结构。本文提出了一个指导框架，从大网络的角度洞悉网络科学领域的主要主题。我们首先从微观，中观，和宏观层面。然后，我们讨论大型网络分析的一些最新的高级主题。系统地介绍了大型网络模型和相关方法，包括排序方法，分区方法以及网络嵌入算法。然后回顾了大型网络中的一些典型应用，例如社区检测，链接预测，推荐等。此外，我们还指出了一些需要进一步研究的关键开放问题。