Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation

被引:10
|
作者
Kwak, Doyeon [1 ]
Kim, Wonjoon [2 ]
机构
[1] Korea Adv Inst Sci & Technol, Grad Sch Culture Technol, Daejeon, South Korea
[2] Korea Adv Inst Sci & Technol, Sch Business & Technol Management, Daejeon, South Korea
来源
PLOS ONE | 2017年 / 12卷 / 05期
基金
新加坡国家研究基金会;
关键词
D O I
10.1371/journal.pone.0177729
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks.
引用
收藏
页数:16
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