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

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/486715.do
DOI
10.1371/journal.pone.0177729
Title
Understanding the process of social network evolution: Online-offlineintegrated analysis of social tie formation
Description
This work was supported by the National Research Foundation of KoreaGrant funded by the Korean Government (NRF-2015-S1A3A-2046742) WKhttp://www.nrf.re.kr/.
abstract
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.
provenance
Made available in Cube on 2018-09-28T16:15:00Z (GMT). No. of bitstreams: 0
language
English
author
Kwak, Doyeon
Kim, Wonjoon
orcid
Kim, Wonjoon/0000-0002-2568-2066
accessioned
2018-09-28T16:15:00Z
available
2018-09-28T16:15:00Z
issued
2017
citation
PLOS ONE(12): 5
issn
1932-6203
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/486715.do
Funder
교육부
Funding Program
BK21플러스사업(0.5)
Project ID
1345274388
Jurisdiction
Rep.of Korea
Project Name
Social Big Data Science Research Team
rights
openAccess
type
article


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