发帖
雷达卡
摘要翻译：
在这篇论文中，我们研究了许多方法来探索和理解随着时间演化的网络的大规模结构。我们特别关注引文网络，如论文、专利或法庭案件等文件之间的引用网络。我们描述了三种不同的分析方法，一种基于期望最大化算法，一种基于模块化优化，一种基于特征向量中心性。以美国最高法院的意见之间的引用网络为例，我们展示了这些方法如何揭示网络中的重大结构划分，以及最终三者的结合如何帮助我们对网络的形状形成一个连贯的全貌。
---
英文标题：
《Large-scale structure of time evolving citation networks》
---
作者：
E. A. Leicht, Gavin Clarkson, Kerby Shedden, and M. E. J. Newman
---
最新提交年份：
2007
---
分类信息：
一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
--
一级分类：Physics 物理学
二级分类：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
--
---
英文摘要：
In this paper we examine a number of methods for probing and understanding the large-scale structure of networks that evolve over time. We focus in particular on citation networks, networks of references between documents such as papers, patents, or court cases. We describe three different methods of analysis, one based on an expectation-maximization algorithm, one based on modularity optimization, and one based on eigenvector centrality. Using the network of citations between opinions of the United States Supreme Court as an example, we demonstrate how each of these methods can reveal significant structural divisions in the network, and how, ultimately, the combination of all three can help us develop a coherent overall picture of the network's shape.
---
PDF链接：
https://arxiv.org/pdf/706.0015
京公网安备 11010802022788号
