Googling social interactions: web search engine based social network construction.
Googling social interactions: web search engine based social network construction.
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DOI：
10.1371/journal.pone.0011233
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发表时间：
2010-07-21
期刊：
影响因子：
3.7
通讯作者：
Jeong H
中科院分区：
文献类型：
--
作者：
Lee SH;Kim PJ;Ahn YY;Jeong H
社会网络分析一直是社会学研究的一个主题。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非常有限，并且无法进行大规模分析。最近，自动生成数据的爆炸式增长彻底改变了研究模式。例如，来自所谓的高通量生物实验的大量数据为传统生物学引入了系统或网络观点。那么，“高通量”的社会学数据生成可能吗？在过去的十年里，谷歌已经成为新的互联网范式最有影响力的象征之一，它可能会在这个（现在和即将到来的）数字时代为这种研究提供大量的数据源。我们调查人与人之间的社交网络通过提取信息在Web上，并引入新的工具，分析这种网络的背景下，复杂系统的统计物理或社会物理。作为一个具体的和说明性的例子，第109届美国参议院的成员进行了分析，它表明，建设和分析的方法是适用于各种其他加权网络。
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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DOI：
10.1073/pnas.0705290104
发表时间：
2007-09-18
影响因子：
11.1
作者：
Todd, Peter M.;Penke, Lars;Lenton, Alison P.
通讯作者：
Lenton, Alison P.
DOI：
10.1073/pnas.0601602103
发表时间：
2006-06-06
影响因子：
11.1
作者：
Newman, M. E. J.
通讯作者：
Newman, M. E. J.
DOI：
10.1016/j.physa.2007.07.039
发表时间：
2007-12-01
影响因子：
3.3
作者：
Porter, Mason A.;Mucha, Peter J.;Friend, A. J.
通讯作者：
Friend, A. J.
影响因子：
3
作者：
Cohen AM;Hersh WR;Dubay C;Spackman K
通讯作者：
Spackman K
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R