Degree Centrality, Betweenness Centrality, and Closeness Centrality in Social Network

Zhang, Junlong; Luo, Yu

doi:10.2991/msam-17.2017.68

Cited by 261 publications

(192 citation statements)

References 13 publications

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“…The obtained relational data was used for social network analysis and finding out key actors. Gathered data was analyzed applying Excel and Ucinet software and network measures, that is, closeness centrality, betweenness centrality, indegree centrality, outdegree centrality, and effect size (Bodin et al, 2006; Freeman, 1979; Zhang & Luo, 2017) were estimated.…”

Section: Methodsmentioning

confidence: 99%

Modeling the impact of social network measures on institutional adaptive capacity needed for sustainable governance of water resources

Salajegheh

Jafari

Pourebrahim

2020

Natural Resource Modeling

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Given the hydrological, social, and economic complexity of water resources, planning for evaluation of such resources is extremely challenging. The vulnerability and limitations of water resources in one hand, and the growing demand on the other hand necessitates integrated planning for resource protection. Accordingly, applying Smart partial least squares software for structural equation modeling using the partial least square path modeling, the impact of social network measures upon the adaptive capacity of organizational stakeholders faced with water scarcity in industries of Kerman Province was scrutinized in the present study. To investigate the structural position of actors (organizational stakeholders) within the social network, relevant data was extracted and thereafter used to estimate the dependent variables of research (dimensions of adaptation). It was found out that there is significant correlation between the measures of stakeholders' network and the adaptive capacity. Closeness centrality (access to information sources related to water) was found to have the strongest effect on fair governance and leadership, necessary for higher adaptive capacity. On the contrary, betweenness centrality (revealing intermediary actors capable of consensus building) was found to have a negative effect on the adaptive capacity. In other words, better access to the flow of information related to water resources enhances the adaptive capacity of stakeholders but the presence of actors of high betweenness centrality, who are reluctant to an adaptive governance system, decreases adaptive capacity. Recommendations for Resource Managers: Establishment of a polycentric governance system that requires power sharing, coordination, and collaboration of all involved actors and stakeholders. Considering water rights in exploiting water resources. Instructing and educating executive managers of water industry projects for increasing adaptive capacity against water scarcity. Creating integrated watershed management. Development of social network analysis knowledge for analyzing the relationships of stakeholders and the governance network of natural resources and environment.

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Section: Methodsmentioning

confidence: 99%

Modeling the impact of social network measures on institutional adaptive capacity needed for sustainable governance of water resources

Salajegheh

Jafari

Pourebrahim

2020

Natural Resource Modeling

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“…According to different calculation methods, centrality analysis can be divided into degree centrality analysis, betweenness centrality analysis and closeness centrality analysis [5,6,8] . The corresponding parameter indexes are degree centrality degree, betweenness centrality degree and closeness centrality degree.…”

Section: Analysis Methods and Tools For Scientific Research Collaboramentioning

confidence: 99%

Research on Scientific Collaboration Behavior Based on Centrality and Cohesive Subgroup Analysis

YongYue¹,

Li²,

Bu³

2018

dtcse

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Abstract. With the increasing complexity of scientific research, interdisciplinary, cross-team, and even cross-regional research collaboration have become a trend. How to effectively break through the constraints of individual knowledge to achieve more effective research collaboration has become a focus and topic of concern. From the perspective of author's co-occurrence in the literature, this paper discusses the analysis methods of scientific research collaboration behavior, and analyzes the essential characteristics of scientific research collaboration behavior through the analysis of author centrality and collaboration cohesive subgroup.

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“…Dalam hubungan individu dengan individu, organisasi dengan organisasi, atau individu dan organisasi terdapat hubungan yang disebut network. Setiap hubungan sosial (social network ) terbentuk dari sejumlah node dan terhubung dengan ties (baca:hubungan) yang berbeda-beda, sehingga dapat dikatakan SNA adalah metode untuk menganalisa struktur jaringan sosial dengan berbagai elemen dalam lingkungan sosial yang saling berhubungan [13]. Pada dasarnya SNA mempelajari teori graf dalam menentukan sentralitas dalam sebuah network [14].…”

Section: Metode Penelitianunclassified

“…Pada dasarnya SNA mempelajari teori graf dalam menentukan sentralitas dalam sebuah network [14]. Dalam menentukan central node atau elemen pusat dalam sebuah network, dapat dilalukan dengan menggunakan tiga jenis sentralitas individu [13] [15] yaitu:…”

Section: Metode Penelitianunclassified

Analisis Node dengan Centrality dan Follower Rank pada Twitter

Mailoa¹

2020

RESTI

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Twitter is used to express about something that happened. In Indonesia since 2012, Twitter has been widely used for campaigns during regional or presidential elections. Apart from positive campaigns, negative campaigns and even black campaigns were carried out via Twitter, and tweets become twitwar. Twitter is a social network, so the data can be analyzed using a social network analysis approach. This research was conducted to analyze which nodes (actors) are influential using the degree, between, and closeness centrality methods, while the follower rank method is used for the analysis of popular actors in "# 4niesKingOfDrama". The data were 8895 nodes with 23257 edges taken from January 1 to February 20, 2020. The results showed that Degree Centrality was 212 with the actor who had the highest influence score was the account @ Bangsul__88 and actor @airin_nz was the actor with the highest popularity value with Follower Rank of 0.98211783. This study found that among the 10 main actors with the highest Degree Centrality values, there were several accounts that were buzzer accounts. The node (Actor) with the highest influence value is not necessarily the node with the highest popularity value.

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Degree Centrality, Betweenness Centrality, and Closeness Centrality in Social Network

Cited by 261 publications

References 13 publications

Modeling the impact of social network measures on institutional adaptive capacity needed for sustainable governance of water resources

Modeling the impact of social network measures on institutional adaptive capacity needed for sustainable governance of water resources

Research on Scientific Collaboration Behavior Based on Centrality and Cohesive Subgroup Analysis

Analisis Node dengan Centrality dan Follower Rank pada Twitter

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