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Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman. The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments. Continued improvements in machine learning algorithms likewise have permitted social scientists and entrepreneurs to use novel techniques to identify latent and meaningful patterns of social interaction and evolution in large electronic datasets.

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman. The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments. Continued improvements in machine learning algorithms likewise have permitted social scientists and entrepreneurs to use novel techniques to identify latent and meaningful patterns of social interaction and evolution in large electronic datasets.

'''社会网络分析 Social Network Analysis'''独立于社会系统计算模型的发展，在20世纪70年代和80年代出现于图论、统计学和社会结构的研究中，它作为一种独特的分析方法被社会学家如 James s. Coleman，Harrison White，Linton Freeman，J. Clyde Mitchell，Mark Granovetter，Ronald Burt 和 Barry Wellman 等阐述和采用。在整个1980年代和1990年代，计算和通信技术日益普及，这要求采用诸如网络分析和多级建模等分析技术，这些技术可以扩展到日益复杂和庞大的数据集中。最近的计算社会学没有使用模拟，而是使用网络分析和先进的统计技术来分析大规模计算机数据库中电子代理的行为数据。电子记录，如电子邮件和即时消息记录，万维网上的超链接，移动电话数据，以及 Usenet 上的讨论，使社会科学家能够直接观察社会行为并在多个时间点和多个层次的分析行为，并且不受传统的实证方法，如访谈、观察（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]）为了通顺，删除研究对象/被试：participants）或调查工具的限制。机器学习算法的不断改进同样使得社会科学家和企业家能够使用新技术来识别大型电子数据集中潜在但有意义的社会互动和演化模式。

'''社会网络分析 Social Network Analysis'''独立于社会系统计算模型的发展，在20世纪70年代和80年代出现于图论、统计学和社会结构的研究中，它作为一种独特的分析方法被社会学家如 James s. Coleman，Harrison White，Linton Freeman，J. Clyde Mitchell，Mark Granovetter，Ronald Burt 和 Barry Wellman 等阐述和采用。在整个1980年代和1990年代，计算和通信技术日益普及，这要求采用诸如网络分析和多级建模等分析技术，这些技术可以扩展到日益复杂和庞大的数据集中。最近的计算社会学没有使用模拟，而是使用网络分析和先进的统计技术来分析大规模<font color='blue'>电子服务器构成的</font>计算机数据库中<s>电子代理</s>的行为数据。电子记录，如电子邮件和即时消息记录，万维网上的超链接，移动电话数据，以及 Usenet 上的讨论，使社会科学家能够直接观察社会行为并在多个时间点和多个层次的分析行为，并且不受传统的实证方法，如访谈、观察（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]）为了通顺，删除研究对象/被试：participants）或调查工具的限制。机器学习算法的不断改进同样使得社会科学家和企业家能够使用新技术来识别大型电子数据集中潜在但有意义的社会互动和演化模式。