论文标题
大网络:调查
Big Networks: A Survey
论文作者
论文摘要
网络是在顶点和链接方面表示复杂系统的典型表达形式,其中网络组件之间的交互模式很复杂。网络可以是静态的,不会随着时间的流逝而变化或动态变化。在网络大小爆炸性增加的新情况下,网络分析的并发症是不同的。在本文中,我们介绍了一个名为Big Network的新网络科学概念。大型网络通常是大规模的,具有复杂且高阶的内部结构。本文提出了一个指南框架,从大型网络的角度来看,从网络科学领域的主要主题深入了解。我们首先介绍了来自三个级别的大网络的结构特性,即微型级别,中级级别和宏观水平。然后,我们讨论一些大网络分析的最新高级主题。大型网络模型和相关方法,包括排名方法,分区方法以及网络嵌入算法。然后对大型网络中的一些典型应用进行审查,例如社区检测,链接预测,建议等。此外,我们还指出了一些需要进一步研究的关键开放问题。
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 big network. Big networks are 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.