Comparing Community Structure to Characteristics in Online Collegiate Social Networks
Comparing Community Structure to Characteristics in Online Collegiate Social Networks
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DOI：
10.1137/080734315
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发表时间：
2011-01-01
期刊：
影响因子：
10.2
通讯作者：
Porter, Mason A.
中科院分区：
文献类型：
--
作者：
Red, Veronica;Kelsic, Eric D.;Porter, Mason A.
我们研究了学生的社交网络的结构，通过检查Facebook的“友谊”在美国五所大学在一个单一的时间点的图形。我们调查了每个单机构网络的社区结构，并采用可视化和定量工具，包括标准化的配对计数方法，来测量网络社区和一组自我识别的用户特征（居住地，班级，专业和高中）之间的相关性。我们回顾的基本属性和统计的就业对计数指数和召回，在简化的符号，一个有用的公式的z分数的兰德系数。我们的研究说明了如何检查在类似环境中构建的社交网络的不同实例，强调了联合收割机形成“社区”的社会力量的阵列，并导致对反映离线社会结构的在线社会结构的比较观察。我们计算了不同特征对各个大学社区结构的相对贡献，并比较了不同大学的相对贡献。例如，我们研究的重要性，共同的高中隶属关系在大型州立大学和不同程度的影响，共同的专业可以在不同的大学的社会结构。我们观察到的社区的异质性表明，大学网络通常有多个组织因素，而不是一个单一的主导因素。
We study the structure of social networks of students by examining the graphs of Facebook "friendships" at five U.S. universities at a single point in time. We investigate the community structure of each single-institution network and employ visual and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the basic properties and statistics of the employed pair-counting indices and recall, in simplified notation, a useful formula for the z-score of the Rand coefficient. Our study illustrates how to examine different instances of social networks constructed in similar environments, emphasizes the array of social forces that combine to form "communities," and leads to comparative observations about online social structures, which reflect offline social structures. We calculate the relative contributions of different characteristics to the community structure of individual universities and compare these relative contributions at different universities. For example, we examine the importance of common high school affiliation at large state universities and the varying degrees of influence that common major can have on the social structure at different universities. The heterogeneity of the communities that we observe indicates that university networks typically have multiple organizing factors rather than a single dominant one.