With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here we extend definitions of edge overlap to weighted and directed networks, and present closed-form expressions for the mean and variance of each version for the Erdos-Renyi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks and our derivations provide a statistically rigorous way for comparing edge overlap across networks.
翻译:随着社交媒体网站和手机等不同数字来源提供的行为数据越来越多,现在有可能获得关于不同环境中社会互动结构、力量和方向性的详细信息。虽然网络结构的大多数计量标准传统上仅为非加权和非定向网络确定,但当前网络数据的丰富程度要求将这些计量标准扩大到加权和定向网络。社交网络中的一个基本计量标准是边缘重叠,两个相关个人共享的朋友比例。在这里,我们将边缘重叠定义扩大到加权和定向网络,并针对Erdos-Renyi随机图及其加权和定向对应方的每种版本的平均值和差异提出封闭式表达。我们将这些结果应用于在印度卡纳塔卡南部农村收集的社会网络数据。我们使用分析结果来量化经验社会网络平均重叠与相应随机图表重叠的程度,并比较网络重叠的值。我们的新定义允许计算更复杂的网络的边缘重叠和我们的衍生数据,为比较网络的边缘重叠提供了一种严格的统计方法。