Structural Hole Theory in Social Network Analysis: A Review

Lin, Zihang; Zhang, Yuwei; Gong, Qingyuan; Chen, Yang; Oksanen, Atte; Ding, Aaron Yi

doi:10.1109/tcss.2021.3070321

Cited by 39 publications

(39 citation statements)

References 61 publications

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“…A hierarchy value closer to 0 indicates that the constraint is the same for the node’s relationship with each neighbor, whereas a value closer to 1 indicates that all constraints are concentrated in a single contact. The spanner tends to have higher values of effective size, efficiency, and hierarchy, and lower values of constraint [ 67 ]. We used Ucinet [ 68 ] to compute the above indices for each node in the global user network.…”

Section: Methodsmentioning

confidence: 99%

Opinion Leaders and Structural Hole Spanners Influencing Echo Chambers in Discussions About COVID-19 Vaccines on Social Media in China: Network Analysis

Wang

Zhou

2022

J Med Internet Res

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Background Social media provide an ideal medium for breeding and reinforcing vaccine hesitancy, especially during public health emergencies. Algorithmic recommendation–based technology along with users’ selective exposure and group pressure lead to online echo chambers, causing inefficiency in vaccination promotion. Avoiding or breaking echo chambers largely relies on key users’ behavior. Objective With the ultimate goal of eliminating the impact of echo chambers related to vaccine hesitancy on social media during public health emergencies, the aim of this study was to develop a framework to quantify the echo chamber effect in users’ topic selection and attitude contagion about COVID-19 vaccines or vaccinations; detect online opinion leaders and structural hole spanners based on network attributes; and explore the relationships of their behavior patterns and network locations, as well as the relationships of network locations and impact on topic-based and attitude-based echo chambers. Methods We called the Sina Weibo application programming interface to crawl tweets related to the COVID-19 vaccine or vaccination and user information on Weibo, a Chinese social media platform. Adopting social network analysis, we examined the low echo chamber effect based on topics in representational networks of information, according to attitude in communication flow networks of users under different interactive mechanisms (retweeting, commenting). Statistical and visual analyses were used to characterize behavior patterns of key users (opinion leaders, structural hole spanners), and to explore their function in avoiding or breaking topic-based and attitude-based echo chambers. Results Users showed a low echo chamber effect in vaccine-related topic selection and attitude interaction. For the former, the homophily was more obvious in retweeting than in commenting, whereas the opposite trend was found for the latter. Speakers, replicators, and monologists tended to be opinion leaders, whereas common users, retweeters, and networkers tended to be structural hole spanners. Both leaders and spanners tended to be “bridgers” to disseminate diverse topics and communicate with users holding cross-cutting attitudes toward COVID-19 vaccines. Moreover, users who tended to echo a single topic could bridge multiple attitudes, while users who focused on diverse topics also tended to serve as bridgers for different attitudes. Conclusions This study not only revealed a low echo chamber effect in vaccine hesitancy, but further elucidated the underlying reasons from the perspective of users, offering insights for research about the form, degree, and formation of echo chambers, along with depolarization, social capital, stakeholder theory, user portraits, dissemination pattern of topic, and sentiment. Therefore, this work can help to provide strategies for public health and public opinion managers to cooperate toward avoiding or correcting echo chamber chaos and effectively promoting online vaccine campaigns.

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Section: Methodsmentioning

confidence: 99%

Opinion Leaders and Structural Hole Spanners Influencing Echo Chambers in Discussions About COVID-19 Vaccines on Social Media in China: Network Analysis

Wang

Zhou

2022

J Med Internet Res

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“…Node Topology Centrality Indicator. Various centrality indicators have been proposed to measure the influence of nodes in complex networks: degree centrality (labeled as DC) [19], betweenness centrality (labeled as BC) [20], closeness centrality (labeled as CC) [21], network constraint coefficient (labeled as NCC) in structural hole theory [22], H-index (labeled as H) [23], K-core indicator (labeled as KS) [24], eigenvector centrality (labeled as EC) [25], etc. ,e research idea of designing multi-attribute evaluation metrics is to synthesize multiple node importance information for critical node discovery, so the strategy of selecting subattributes must be based on application requirements.…”

Section: Multiattribute Decision-making Method-criticmentioning

confidence: 99%

Research on the Identification of Internet Critical Nodes Based on Multilayer Network Modeling

Zhang

Yang

et al. 2022

Security and Communication Networks

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The research goal of cyberspace security situational awareness analysis is to predict the future security development of the target network by acquiring, understanding, and displaying the security elements in the large-scale network environment. Current cyberspace security situational awareness systems are mostly based on traditional single-layer network topology to analyze the security of the target network's operational posture. However, as the scale of the network continues to expand, the network structure becomes more complex, and the information fusion in multiple fields in practical applications deepens, the single-layer topology model can no longer meet the analysis requirements. In this paper, we construct a multilayer network topology model for cyberspace security situational awareness by integrating multidimensional information in the physical device layer network, business application layer network, and user role layer network. Meanwhile, to eliminate the limitations of traditional node importance indicators, a node importance assessment indicator that integrates topological centrality and node dependency factor is proposed in conjunction with model characteristics: multilayer dependency CRITIC indicator ( MDCI ). On the one hand, MDCI fits a variety of evaluation metrics through the CRITIC multi-attribute decision method to comprehensively assess the importance of nodes in network centrality, and on the other hand, MDCI better aggregates the important contributions of nodes in each network layer based on node dependency factor to coordinate multilayer network information. The experimental results show that MDCI has better ordering monotonicity and generates more stable metric sequences, and can effectively cause large-scale failures in multilayer network while destroying fewer physical device components, which can be better adapted to the critical node identification needs of multilayer network.

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Section: Interdisciplinary Subject Interactionmentioning

confidence: 99%

“…Research on interdisciplinary subject interaction [16,17] can cast light on the overall logic of discipline development. In 2019, Frank et al [17] studied the evolution citation graph of Artificial Intelligence (AI) research. They analyzed the association between various academic fields and AI research through the referencing relationship of papers published in each academic field.…”

Section: Interdisciplinary Subject Interactionmentioning

confidence: 99%

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Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

Gong

et al. 2022

J. Soc. Comput.

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During the past decades, the term "social computing" has become a promising interdisciplinary area in the intersection of computer science and social science. In this work, we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project (DBLP), a representative computer science bibliography website. We have observed a series of trends in the development of social computing, including the evolution of the number of publications, popular keywords, top venues, international collaborations, and research topics. Our findings will be helpful for researchers and practitioners working in relevant fields.

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Structural Hole Theory in Social Network Analysis: A Review

Cited by 39 publications

References 61 publications

Opinion Leaders and Structural Hole Spanners Influencing Echo Chambers in Discussions About COVID-19 Vaccines on Social Media in China: Network Analysis

Opinion Leaders and Structural Hole Spanners Influencing Echo Chambers in Discussions About COVID-19 Vaccines on Social Media in China: Network Analysis

Research on the Identification of Internet Critical Nodes Based on Multilayer Network Modeling

Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

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