加权和定向社交网络中的边缘重叠

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY Network Science Pub Date : 2021-06-01 Epub Date: 2021-02-16 DOI:10.1017/nws.2020.49
Heather Mattie, Jukka-Pekka Onnela
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引用次数: 0

摘要

随着来自社交媒体网站和手机等各种数字来源的行为数据越来越多,我们现在有可能获得有关各种环境下社会互动的结构、强度和方向性的详细信息。虽然大多数网络结构指标传统上只针对无权和无向网络,但当前网络数据的丰富性要求将这些指标扩展到有权和有向网络。社交网络中的一个基本指标是边缘重叠度,即两个相连个体共享的好友比例。在这里,我们将边缘重叠的定义扩展到了加权网络和有向网络,并给出了 Erdős-Rényi 随机图及其加权和有向对应图的每个版本的均值和方差的闭式表达式。我们将这些结果应用于在印度卡纳塔克邦南部农村收集的社会网络数据。我们利用分析结果量化了经验社会网络的平均重合度与相应随机图的重合度的偏差程度,并比较了不同网络的重合度值。我们的新定义允许计算更复杂网络的边缘重叠度,我们的推导为比较不同网络的边缘重叠度提供了严格的统计方法。
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Edge Overlap in Weighted and Directed Social Networks.

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 Erdős-Rényi 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.

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来源期刊
Network Science
Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
3.50
自引率
5.90%
发文量
24
期刊介绍: Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.
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