摘要翻译：
社会网络分析长期以来一直是社会学研究的一个不懈的课题。然而，直到信息技术时代，主要通过传统的个人调查方法收集的数据的可用性受到高度限制，并阻止了大规模分析。最近，自动生成的数据量爆炸式增长，彻底改变了研究的模式。例如，所谓的高通量生物实验所产生的大量数据，给传统生物学引入了系统或网络的观点。那么，“高通量”的社会学数据生成是否可能？在过去的十年里，谷歌已经成为互联网新范式最具影响力的标志之一，它可能为这个（现在和即将到来的）数字时代的研究提供大量的数据源。我们通过提取Web上的信息来研究人与人之间的社会网络，并在复杂系统的统计物理或社会物理的背景下引入分析这种网络的新工具。作为一个具体的说明性例子，对美国第109届参议院的成员进行了分析，证明了这种构造和分析方法也适用于其他各种加权网络。
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英文标题：
《Googling Social Interactions: Web Search Engine Based Social Network
  Construction》
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作者：
Sang Hoon Lee, Pan-Jun Kim, Yong-Yeol Ahn, and Hawoong Jeong
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最新提交年份：
2010
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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。
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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
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英文摘要：
  Social network analysis has long been an untiring topic of sociology. However, until the era of information technology, the availability of data, mainly collected by the traditional method of personal survey, was highly limited and prevented large-scale analysis. Recently, the exploding amount of automatically generated data has completely changed the pattern of research. For instance, the enormous amount of data from so-called high-throughput biological experiments has introduced a systematic or network viewpoint to traditional biology. Then, is "high-throughput" sociological data generation possible? Google, which has become one of the most influential symbols of the new Internet paradigm within the last ten years, might provide torrents of data sources for such study in this (now and forthcoming) digital era. We investigate social networks between people by extracting information on the Web and introduce new tools of analysis of such networks in the context of statistical physics of complex systems or socio-physics. As a concrete and illustrative example, the members of the 109th United States Senate are analyzed and it is demonstrated that the methods of construction and analysis are applicable to various other weighted networks.
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PDF链接：
https://arxiv.org/pdf/710.3268