Tourist destination development and social network analysis: What does degree centrality contribute?

2021 ◽  
Author(s):  
Oswaldo Ledesma González ◽  
Rafael Merinero‐Rodríguez ◽  
Juan Ignacio Pulido‐Fernández
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Degree Centrality ◽  
Tourist Destination

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

2018 ◽  
Vol 8 (4) ◽  
pp. 291 ◽  
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.

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

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 

Communication Network Analysis of Project Teams in Korean Building Constructions

2013 ◽  
Vol 357-360 ◽  
pp. 2338-2341 ◽  
Author(s):  
Jae Yeob Kim ◽  
Sang Tae No ◽  
Yong Kyu Park
Keyword(s):  
Communication Network ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Construction Project ◽  
Project Teams ◽  
Degree Centrality ◽  
Analysis Program ◽  
Team Members ◽  
Communication Frequency

This study used social network analysis (SNA) in order to analyze communication relationship between project team members in typical cases of Korean building constructions. Data was collected by conducting a survey from key members of construction project teams. We analyzed and digitized degree centrality by using Netminer, a SNA analysis program. According to the result of analysis in communication frequency, intermediate managers such as construction deputy managers were shown the highest and architectural designers were shown the lowest. With respect to communication credibility, construction managers were shown the highest and architectural designers were shown to be low. We discovered that intermediate managers and construction managers of the construction teams play important role in the communication of project teams.

scholarly journals Mal-Netminer: Malware Classification Approach Based on Social Network Analysis of System Call Graph

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Jae-wook Jang ◽  
Jiyoung Woo ◽  
Aziz Mohaisen ◽  
Jaesung Yun ◽  
Huy Kang Kim
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Clustering Coefficient ◽  
Component Ratio ◽  
System Call ◽  
Degree Centrality ◽  
Call Graph ◽  
System Calls ◽  
Graph Metrics

As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that “influence-based” graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.

Analisis Node dengan Centrality dan Follower Rank pada Twitter

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.

Filling structural holes? Guanxi-based facilitation of knowledge sharing within a destination network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiayuan Liu ◽  
Jianzhou Yan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Knowledge Sharing ◽  
Design Methodology ◽  
Network Structures ◽  
Structured Interviews ◽  
Tourist Destination ◽  
Structural Holes ◽  
Content Type

PurposeThis study examines the relationships between structural holes, guanxi and knowledge sharing among groups of stakeholders within a Chinese destination network.Design/methodology/approachThis study conducted surveys, social network analysis and semi-structured interviews to gather data from the stakeholders of a popular Chinese tourist destination to test its hypotheses.FindingsKnowledge sharing within the destination network was impeded by structural holes but facilitated by guanxi. Furthermore, the impeding effect of structural holes on knowledge sharing is alleviated by guanxi.Originality/valueThis study illustrates the ways that stakeholders exploit structural holes and guanxi to promote knowledge sharing, and thus offers novel insights into how destination network structures affect the efficacy of stakeholders when it comes to sharing knowledge and promoting their destination.

scholarly journals Social network analysis methods for exploring SARS-CoV-2 contact tracing data

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Karikalan Nagarajan ◽  
Malaisamy Muniyandi ◽  
Bharathidasan Palani ◽  
Senthil Sellappan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Disease Transmission ◽  
Contact Tracing ◽  
Centrality Measures ◽  
Degree Centrality ◽  
Individual Patient Level ◽  
Patient Level ◽  
Network Measures

Abstract Background Contact tracing data of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is used to estimate basic epidemiological parameters. Contact tracing data could also be potentially used for assessing the heterogeneity of transmission at the individual patient level. Characterization of individuals based on different levels of infectiousness could better inform the contact tracing interventions at field levels. Methods Standard social network analysis methods used for exploring infectious disease transmission dynamics was employed to analyze contact tracing data of 1959 diagnosed SARS-CoV-2 patients from a large state of India. Relational network data set with diagnosed patients as “nodes” and their epidemiological contact as “edges” was created. Directed network perspective was utilized in which directionality of infection emanated from a “source patient” towards a “target patient”. Network measures of “ degree centrality” and “betweenness centrality” were calculated to identify influential patients in the transmission of infection. Components analysis was conducted to identify patients connected as sub- groups. Descriptive statistics was used to summarise network measures and percentile ranks were used to categorize influencers. Results Out-degree centrality measures identified that of the total 1959 patients, 11.27% (221) patients have acted as a source of infection to 40.19% (787) other patients. Among these source patients, 0.65% (12) patients had a higher out-degree centrality (> = 10) and have collectively infected 37.61% (296 of 787), secondary patients. Betweenness centrality measures highlighted that 7.50% (93) patients had a non-zero betweenness (range 0.5 to 135) and thus have bridged the transmission between other patients. Network component analysis identified nineteen connected components comprising of influential patient’s which have overall accounted for 26.95% of total patients (1959) and 68.74% of epidemiological contacts in the network. Conclusions Social network analysis method for SARS-CoV-2 contact tracing data would be of use in measuring individual patient level variations in disease transmission. The network metrics identified individual patients and patient components who have disproportionately contributed to transmission. The network measures and graphical tools could complement the existing contact tracing indicators and could help improve the contact tracing activities.

scholarly journals Big Data Analysis of the Key Attributes Related to Stress and Mental Health in Korean Taekwondo Student Athletes

2022 ◽  
Vol 14 (1) ◽  
pp. 477
Author(s):  
Sung-Un Park ◽  
Jung-Woo Jeon ◽  
Hyunkyun Ahn ◽  
Yoon-Kwon Yang ◽  
Wi-Young So
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Correlation Analysis ◽  
Student Athletes ◽  
Big Data Analysis ◽  
Degree Centrality

In the present study, we used big data analysis to examine the key attributes related to stress and mental health among Korean Taekwondo student-athletes. Keywords included “Taekwondo + Student athlete + Stress + Mental health”. Naver and Google databases were searched to identify research published between 1 January 2010 and 31 December 2019. Text-mining analysis was performed on unstructured texts using TEXTOM 4.5, with social network analysis performed using UCINET 6. In total, 3149 large databases (1.346 MB) were analyzed. Two types of text-mining analyses were performed, namely, frequency analysis and term frequency-inverse document frequency analysis. For the social network analysis, the degree centrality and convergence of iterated correlation analysis were used to deduce the node-linking degree in the network and to identify clusters. The top 10 most frequently used terms were “stress”, “Taekwondo”, “health”, “player”, “student”, “mental”, “exercise”, “mental health”, “relieve”, and “child.” The top 10 most frequently occurring results of the TF-IDF analysis were “Taekwondo”, “health”, “player”, “exercise”, “student”, “mental”, “stress”, “mental health”, “child” and “relieve”. The degree centrality analysis yielded similar results regarding the top 10 terms. The convergence of iterated correlation analysis identified six clusters: student, start of dream, diet, physical and mental, sports activity, and adult Taekwondo center. Our results emphasize the importance of designing interventions that attenuate stress and improve mental health among Korean Taekwondo student-athletes.

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