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

Peristiwa banjir bandang yang diakibatkan Siklon Seroja telah mengundang perhatian dan simpati masyarakat Indonesia. Berbagai upaya telah dilakukan untuk berkontribusi dalam upaya penanggulangan dampak yang diterima oleh masyarakat NTT. Salah satu upaya yang dilakukan oleh masyarakat adalah mengampanyekan gerakan sosial digital #SolidaritasUntukNTT di Twitter. Gerakan sosial digital melalui pesan-pesan tertentu dapat menggugah kesadaran bagi penggunanya. Untuk mengetahui efektivitas penyebaran pesan dalam gerakan sosial digital dapat divisualisasikan menggunakan metode Social Network Analysis (SNA). Penelitian ini bertujuan untuk memvisualisasikan peran pers dalam mendistribusikan pesan gerakan sosial digital dengan tagar #SolidaritasUntukNTT. Metode penelitian yang digunakan adalah analisis jaringan sosial dengan teori graf di Twitter. Hasil analisis dan visualisasi jaringan dilakukan di aplikasi Gephi dengan algoritma Yifan Hu untuk melihat distribusi pola pesan dan peran pers pada tagar #SolidaritasUntukNTT. Penelitian ini menggambarkan tipe jaringan two mode yang terdiri dari interaksi antara individu dan organisasi dengan pola komunikasi radial personal network yang memiliki ciri jaringan terbuka dan kohesivitas yang rendah dengan arah relasi directed dan asimetris. Analisis peran pers diukur melalui sentralitas aktor untuk mengetahui degree centrality, closeness centrality, betweenness centrality dan eigenvector centrality. Aktor @vice_id diketahui sebagai aktor yang memiliki degree dan eigenvector centrality tertinggi dibandingkan dengan aktor pers lainnya. Aktor @idntimes dan @detikcom memiliki nilai closeness dan betweenness centrality yang lebih tinggi dari media lainnya. Analisis jaringan sosial memberikan pemahaman terkait distribusi pesan dalam media sosial untuk mengetahui efektivitas pesan yang didistribusikan oleh beberapa aktor jaringan, khususnya peran pers dalam mengampanyekan gerakan sosial di media. Oleh karena itu, metode SNA dapat digunakan untuk penelitian jurnalisme data.

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A network analysis of character relations in Etienne van Heerden’s Toorberg

Literator ◽

10.4102/lit.v34i2.434 ◽

2013 ◽

Vol 34 (2) ◽

Cited By ~ 1

Author(s):

Burgert A. Senekal

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Betweenness Centrality ◽

Theoretical Framework ◽

Closeness Centrality ◽

Degree Centrality ◽

Eigenvector Centrality ◽

Traditional Concept ◽

Family Ties

Etienne van Heerden’s Toorberg can be approached as a modern, postcolonial farm novel, partly because it challenges the concept of lineage of inheritance, which is characteristic of the traditional farm novel. Lineage of inheritance implies a strong family bond, and it is therefore instructive to investigate how family ties function within this novel. The article views family ties within Toorberg using Social Network Analysis (SNA), a largely unknown theoretical framework that can also be applied within the study of literature. It is shown how characters’ positions in this network can be calculated in terms of degree centrality, closeness centrality, Eigenvector centrality and betweenness centrality, and how these measures expose the way in which this novel undermines the traditional concept of inheritance.

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Using Networks to Measure Influence and Impact

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.2668 ◽

2014 ◽

Vol 556-562 ◽

pp. 2668-2671

Author(s):

Li Xian Zhang ◽

Yu Jia Liu ◽

Xin Zhong Lu

Keyword(s):

Social Network ◽

Complex Network ◽

Calculation Result ◽

Betweenness Centrality ◽

Small World ◽

Closeness Centrality ◽

Degree Centrality ◽

Scale Free ◽

Important Type ◽

Ranking Order

The co-author networks are important type of social network. In this paper, we establishes the Erdös co-author network and proves that the Erdös co-author network is a complex network which has three main properties, including small world, scale-free and clustering properties. Besides, this article gives the calculation formulas for degree centrality, closeness centrality and betweenness centrality of a network. According to the calculation result we give a ranking order for authors within Erdös co-author network.

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The Social Engagement to Agricultural Issues using Social Network Analysis

IJID (International Journal on Informatics for Development) ◽

10.14421/ijid.2021.2185 ◽

2021 ◽

Vol 10 (1) ◽

pp. 1-7

Author(s):

Tanty Yanuar Widiyanti ◽

Teguh Bharata Adji ◽

Indriana Hidayah

Keyword(s):

Social Media ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Social Engagement ◽

Betweenness Centrality ◽

Closeness Centrality ◽

Degree Centrality ◽

Eigenvector Centrality ◽

The Government

Twitter is one of the micro-blogging social media which emphasizes the speed of communication. In the 4.0 era, the government also promotes the distribution of information through social media to reach the community from various lines. In previous research, Social Network Analysis was used to see the relationship between actors in a work environment, or as a basis for identifying the application of technology adoption in decision making, whereas no one has used SNA to see trends in people's response to agricultural information. This study aims to see the extent to which information about agriculture reaches the community, as well as to see the community's response to take part in agricultural development. This article also shows the actors who took part in disseminating information. Data was taken on November 13 to 20, 2020 from the Drone Emprit Academic, and was taken limited to 3000 nodes. Then, the measurements of the SNA are represented on the values of Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality. @AdrianiLaksmi has the highest value in Eigenvector Centrality and Degree Centrality, he has the greatest role in disseminating information and has many followers among other accounts that spread the same information. While the @RamliRizal account ranks the highest in Betweenness Centrality, who has the most frequently referred information, and the highest Closeness Centrality is owned by the @baigmac account because of the fastest to re-tweet the first information.

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A study on the influence of Korean Middle School students' relationship through Science class applying STAD cooperative learning

Journal of Technology and Science Education ◽

10.3926/jotse.407 ◽

2018 ◽

Vol 8 (4) ◽

pp. 291 ◽

Cited By ~ 1

Author(s):

Dongryeul Kim

Keyword(s):

Middle School ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Middle School Students ◽

Collaborative Learning ◽

Closeness Centrality ◽

Degree Centrality ◽

Science Class ◽

School Students

In order to find out the influence of Korean Middle School Students' relationship by science class applying STAD collaborative learning, this study conducted a social network analysis and sought to analyze the communication networks within the group and identified the change process of the type. The subject of this study was 30 students of the second grade at the girls' middle school located in Korea's Metropolitan City. For five weeks, science class applying STAD Collaborative Learning was implemented in the ‘reproduction and generation’ chapter. First, the class social network analysis showed that all the prices of density, degree centrality, closeness centrality, and betweenness centrality have risen after science class applying STAD Collaborative Learning. Also, the classroom's relationship index has improved. In other words, STAD Collaborative Learning encouraged interaction among students. Second, in order to research popularity, students' centrality analysis through the class social network analysis showed that top-ranked students' values of density, degree centrality, closeness centrality, and betweenness centrality appeared commonly high after science class applying STAD Collaborative Learning. Third, the analysis of the communication network change within six groups showed that all channel type appeared most often and circle type also appeared anew after science class applying STAD Collaborative Learning. In other words, it was possible to exchange information freely and communicate with all members of the group through STAD Collaborative Learning.

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Unpacking the “Black Box”: Understanding the Effect of Social Network Characteristics on Safety Behaviors of Construction Workers

Complexity ◽

10.1155/2020/8836682 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Shuquan Li ◽

Pei Yang ◽

Xiuyu Wu ◽

Ge Wang ◽

Meng Fan

Keyword(s):

Social Network ◽

Closeness Centrality ◽

Construction Workers ◽

Safety Behaviors ◽

Degree Centrality ◽

Construction Project Management ◽

Informal Networks ◽

Safety Communication ◽

Network Characteristics ◽

Safety Compliance

How to improve the safety behaviors of construction workers has dogged the realm of construction project management. Previous studies mainly focused on the individual and/or organizational factors shaping safety behaviors, while there is a dearth of research focusing on the effect of individual-organizational nexus (i.e., the network embeddedness of individuals within the organization). Thus, this study employs social network analysis (SNA) and multivariable regression analyses to explore the relationship between the characteristics of social networks of construction workers (i.e., degree, closeness, and betweenness centralities) and their safety behaviors (i.e., safety participation and safety compliance), considering the mediating role of safety communication. The primary data were collected from ten Chinese construction projects. The results include the following three aspects. First, degree centrality, closeness centrality, and betweenness centrality all exert significant positive effects on safety participation. Closeness centrality yields a positive effect on safety compliance in formal networks. Degree centrality has a positive effect on both safety compliance and safety participation, whereas the other two centrality characteristics exhibit no significant effect in informal networks. Second, in formal networks, safety communication plays a partial mediation role between closeness centrality and safety compliance and a full mediation role between degree and closeness centralities and safety participation. Third, in informal networks, safety communication plays a full mediation role between degree centrality and safety compliance and a partial mediation role between degree centrality and safety participation. This study provides new insights for construction project management in achieving improved safety performance via shaping the social network characteristics.

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PEMETAAN PERILAKU BERISIKO GAY BERBASIS SNA SEBAGAI EARLY WARNING SYSTEM KASUS HIV/AIDS DI KOTA KENDARI

Preventif Journal ◽

10.37887/epj.v4i1.9491 ◽

2019 ◽

Vol 4 (1) ◽

Author(s):

Jumartin Gerung

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Closeness Centrality ◽

Degree Centrality ◽

Betweeness Centrality ◽

Hiv Aids

AbstrakPada kasus HIV dalam skala nasional, menunjukkan bahwa kelompok heteroseks juga termasuk sebagai kelompokutama yang paling berisiko menderita HIV/AIDS. Peningkatan ini mencolok terijadi sejak 2015 angkanya masih di 4.241 kasus, dan meningkat hingga lebih dari dua kali lipat pada 2016 yang mencapai 13.063 kasus. Data pemetaaninteraksi di sosial media khususnya wilayah Kendari terdapat sekitar 800 akun yang memberi interaksi perihal Gay.Hal ini diindikasikan akan mempengaruhi prevalensi kejadian HIV/AIDS di Kota Kendari. Penelitian ini bertujuan untukmemetakan interaksi perilaku berisiko Gay sebagai early warning system kasus HIV/AIDS. Social Network Analysismerupakan studi yang mempelajari tentang hubungan manusia dengan memanfaatkan teori graf. Penerapan SocialNetworkAnalysis dalam suatu aplikasi mampu menggambarkan relasi atau hubungan antar individu denganmelakukan visualisasi terkait centrality (titik pusat), between centrality (jalur pendek), juga closeness centrality yaknirata-rata jalur terpendek dari interaksi akun di laman FB. Untuk platform Facebook berdasarkan pada hasilpenghitungan diketahui bahwa akun yang berpengaruh terhadap interaksi jejaring sosial adalah akun Gay Kendariyang unggul pada nilai degree centrality,betweeness centrality, dan Closeness centrality. Akun Gay Kendari palingberpengaruh dalam interaksi jaringan sosial Facebook. Melalui social network analysis, penelitian ini memberikangambaran relasi perilaku berisiko LSL/Gay sebagai early warning system kasus HIV/AIDS di kota kendariKata kunci: analisis jaringan sosiai, gay, sistem peringatan dini, HIV/AIDS

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EKSPLORASI PEMIMPIN OPINI UNTUK ALTERNATIF PENDUKUNG PEMASARAN PT. NET MEDIATAMA INDONESIA MENGGUNAKAN METODE ANALISIS JEJARING SOSIAL DI TWITTER

SOSIOHUMANITAS ◽

10.36555/sosiohumanitas.v21i1.1002 ◽

2019 ◽

Vol 21 (1) ◽

pp. 73-78

Author(s):

Dian Puteri Ramadhani ◽

Andry Alamsyah ◽

Mukti Bawono Wicaksono

Keyword(s):

Betweenness Centrality ◽

Application Programming Interface ◽

Closeness Centrality ◽

Degree Centrality ◽

Application Programming ◽

Programming Interface

Pertumbuhan pesat teknologi di era globalisasi mengakibatkan pertukaran informasi tidak hanya terjadi pada dunia nyata. Internet telah menjadi kebutuhan pokok dalam menyebarkan informasi. Pertumbuhan penguna internet meningkatkan jumlah data yang beredar di seluruh dunia. Data interaksi yang pada media sosial dapat digunakan untuk melihat bagaimana suatu hal diperbincangkan. Penelitian ini bertujuan untuk menemukan aktor yang paling berperan dalam jaringan PT. Net Mediatama Indonesia di media sosial Twitter. Penelitian ini memanfaatkan sejumlah besar data yang diambil dari Twitter melalui Application Programming Interface. Data tersebut diteliti dengan pendekatan analisis jejaring sosial. Visualisasi dan perhitungan dilakukan menggunakan software Gephi. Aktor penting ditentukan berdasarkan degree centrality, closeness centrality, dan betweenness centrality. Pemain kunci dalam jaringan NET yaitu @chuuattac sehingga akun tersebut merupakan pemimpin opini yang pendapatnya didengarkan, dipercaya, dan membuat aktor lain bereaksi. Akun tersebut dapat digunakan sebagai alternatif pendukung pemasaran dalam mengkampanyekan produk dan menyebarkan informasi NET dengan lebih cepat dan tepat sasaran.

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Analisis Node dengan Centrality dan Follower Rank pada Twitter

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i5.2398 ◽

2020 ◽

Vol 4 (5) ◽

pp. 937-942

Author(s):

Evangs Mailoa

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Presidential Elections ◽

Closeness Centrality ◽

Analysis Approach ◽

Degree Centrality ◽

Negative Campaigns

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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Efficiency centrality in time-varying graphs

Acta Universitatis Sapientiae Informatica ◽

10.2478/ausi-2020-0001 ◽

2020 ◽

Vol 12 (1) ◽

pp. 5-21

Author(s):

Péter Marjai ◽

Attila Kiss

Keyword(s):

Complex Networks ◽

Betweenness Centrality ◽

Cascade Model ◽

Closeness Centrality ◽

Degree Centrality ◽

Time Varying ◽

Independent Cascade Model ◽

Independent Cascade ◽

Influential Nodes ◽

Time Varying Graphs

AbstractOne of the most studied aspect of complex graphs is identifying the most influential nodes. There are some local metrics like degree centrality, which is cost-effiective and easy to calculate, although using global metrics like betweenness centrality or closeness centrality can identify influential nodes more accurately, however calculating these values can be costly and each measure has it’s own limitations and disadvantages. There is an ever-growing interest in calculating such metrics in time-varying graphs (TVGs), since modern complex networks can be best modelled with such graphs. In this paper we are investigating the effectiveness of a new centrality measure called efficiency centrality in TVGs. To evaluate the performance of the algorithm Independent Cascade Model is used to simulate infection spreading in four real networks. To simulate the changes in the network we are deleting and adding nodes based on their degree centrality. We are investigating the Time-Constrained Coverage and the magnitude of propagation resulted by the use of the algorithm.

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