• Title, Summary, Keyword: Closeness Centrality

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The Relationships among Network Centrality, Psychological Well-being, and Intention to Exercise Maintenance in Participants of an Aquatic Exercise Program (수중운동 프로그램 참여자의 네트워크 중심성과 심리적 안녕감, 운동지속의도와의 관계)

  • Won, Hyo Jin;Kim, Jong Im
    • Journal of muscle and joint health
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    • v.22 no.1
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    • pp.13-19
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    • 2015
  • Purpose: The purpose of this study was to identify the relationships among network centrality, psychological well-being (PWBS), and intention to exercise maintenance in participants of an aquatic exercise program. Methods: Using a single-experimental design, 17 osteoarthritis patients participated in an aquatic exercise program. The questionnaire to connect the network of members was used to peer nomination by Moreno (1953). Data were analyzed with the UCINET using centrality (degree, closeness, betweenness) and SPSS using descriptive statistics, wilcoxon signed ranked test, and spearman's rho. Results: Closeness centrality, PWBS, and intention to exercise maintenance were significantly different between 4 weeks and 8 weeks. At 4 weeks, PWBS was positively correlated with closeness centrality. Intention to exercise maintenance was positively correlated with degree, closeness, and betweenness centrality. At 8 weeks, PWBS was positively correlated with closeness centrality. Intention to exercise maintenance was positively correlated with closeness centrality. Conclusion: The aquatic exercise program can be effective in increasing closeness centrality, psychological well-being, and intention to exercise maintenance. This was the first study attempted to analyze construction of member relationships in osteoarthritis patients participating an exercise program by using social network analysis.

An Estimated Closeness Centrality Ranking Algorithm and Its Performance Analysis in Large-Scale Workflow-supported Social Networks

  • Kim, Jawon;Ahn, Hyun;Park, Minjae;Kim, Sangguen;Kim, Kwanghoon Pio
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.3
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    • pp.1454-1466
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    • 2016
  • This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.

A Calculation Method of Closeness Centrality for High Density Wireless Sensor Networks

  • Dehkanov, Shuhrat;Kim, Young-Rag;Lee, Bok-Man;Kim, Chong-Gun
    • 한국정보컨버전스학회:학술대회논문집
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    • pp.43-46
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    • 2008
  • Centrality has been actively studied in network analysis field. In this paper we show a calculation method of closeness centrality for WSN. Since nodes in a sensor network are very scarce in energy and computation capability the calculation of the closeness is done in two tiers by dividing network into clusters. In first step closeness centrality for cluster heads is calculated. In the second step closeness of member nodes of the chosen cluster is computed in respect to that cluster itself.

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A Closeness Centrality Analysis Algorithm for Workflow-supported Social Networks (워크플로우 소셜 네트워크 근접중심성 분석 알고리즘)

  • Park, Sungjoo;Kim, Kwanghoon Pio
    • Journal of Internet Computing and Services
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    • v.14 no.5
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    • pp.77-85
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    • 2013
  • This paper proposes a closeness centrality analysis algorithm for workflow-supported social networks that represent the collaborative relationships among the performers who are involved in a specific workflow model. The proposed algorithm uses the social network analysis techniques, particularly closeness centrality equations, to analyze the closeness centrality of the workflow-supported social network. Additionally, through an example we try to verify the accuracy and appropriateness of the proposed algorithm.

Monte-Carlo Methods for Social Network Analysis (사회네트워크분석에서 몬테칼로 방법의 활용)

  • Huh, Myung-Hoe;Lee, Yong-Goo
    • The Korean Journal of Applied Statistics
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    • v.24 no.2
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    • pp.401-409
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    • 2011
  • From a social network of n nodes connected by l lines, one may produce centrality measures such as closeness, betweenness and so on. In the past, the magnitude of n was around 1,000 or 10,000 at most. Nowadays, some networks have 10,000, 100,000 or even more than that. Thus, the scalability issue needs the attention of researchers. In this short paper, we explore random networks of the size around n = 100,000 by Monte-Carlo method and propose Monte-Carlo algorithms of computing closeness and betweenness centrality measures to study the small world properties of social networks.

Internet Worm Propagation Model Using Centrality Theory

  • Kwon, Su-Kyung;Choi, Yoon-Ho;Baek, Hunki
    • Kyungpook Mathematical Journal
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    • v.56 no.4
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    • pp.1191-1205
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    • 2016
  • The emergence of various Internet worms, including the stand-alone Code Red worm that caused a distributed denial of service (DDoS), has prompted many studies on their propagation speed to minimize potential damages. Many studies, however, assume the same probabilities for initially infected nodes to infect each node during their propagation, which do not reflect accurate Internet worm propagation modelling. Thus, this paper analyzes how Internet worm propagation speed varies according to the number of vulnerable hosts directly connected to infected hosts as well as the link costs between infected and vulnerable hosts. A mathematical model based on centrality theory is proposed to analyze and simulate the effects of degree centrality values and closeness centrality values representing the connectivity of nodes in a large-scale network environment on Internet worm propagation speed.

An Estimated Closeness Centrality Ranking Algorithm for Large-Scale Workflow Affiliation Networks (대규모 워크플로우 소속성 네트워크를 위한 근접 중심도 랭킹 알고리즘)

  • Lee, Do-kyong;Ahn, Hyun;Kim, Kwang-hoon Pio
    • Journal of Internet Computing and Services
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    • v.17 no.1
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    • pp.47-53
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    • 2016
  • A type of workflow affiliation network is one of the specialized social network types, which represents the associative relation between actors and activities. There are many methods on a workflow affiliation network measuring centralities such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In particular, we are interested in the closeness centrality measurements on a workflow affiliation network discovered from enterprise workflow models, and we know that the time complexity problem is raised according to increasing the size of the workflow affiliation network. This paper proposes an estimated ranking algorithm and analyzes the accuracy and average computation time of the proposed algorithm. As a result, we show that the accuracy improves 47.5%, 29.44% in the sizes of network and the rates of samples, respectively. Also the estimated ranking algorithm's average computation time improves more than 82.40%, comparison with the original algorithm, when the network size is 2400, sampling rate is 30%.

Performance Analysis of an Estimated Closeness Centrality Ranking Algorithm in Large-Scale Workflow-supported Social Networks (대규모 워크플로우 소셜 네트워크의 추정 근접 중심도 랭킹 알고리즘 성능 분석)

  • Kim, Jawon;Ahn, Hyun;Kim, Kwanghoon
    • Journal of Internet Computing and Services
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    • v.16 no.3
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    • pp.71-77
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    • 2015
  • This paper implements an estimated closeness centrality ranking algorithm in large-scale workflow-supported social networks and performance analyzes of the algorithm. Existing algorithm has a time complexity problem which is increasing performance time by network size. This problem also causes ranking process in large-scale workflow-supported social networks. To solve such problems, this paper conducts comparison analysis on the existing algorithm and estimated results by applying estimated-driven RankCCWSSN(Rank Closeness Centrality Workflow-supported Social Network). The RankCCWSSN algorithm proved its time-efficiency in a procedure about 50% decrease.

Curve Estimation among Citation and Centrality Measures in Article-level Citation Networks (문헌 단위 인용 네트워크 내 인용과 중심성 지수 간 관계 추정에 관한 연구)

  • Yu, So-Young
    • Journal of the Korean Society for information Management
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    • v.29 no.2
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    • pp.193-204
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    • 2012
  • The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

Analysis of Seoul Metropolitan Subway Network Characteristics Using Network Centrality Measures (네트워크 중심성 지표를 이용한 서울 수도권 지하철망 특성 분석)

  • Lee, Jeong Won;Lee, Kang Won
    • Journal of the Korean Society for Railway
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    • v.20 no.3
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    • pp.413-422
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    • 2017
  • In this study we investigate the importance of the subway station using network centrality measures. For centrality measures, we have used betweenness centrality, closeness centrality, and degree centrality. A new measure called weighted betweenness centrality is proposed, that combines both traditional betweenness centrality and passenger flow between stations. Through correlation analysis and power-law analysis of passenger flow on the Seoul metropolitan subway network, we have shown that weighted betweenness centrality is a meaningful and practical measure. We have also shown that passenger flow between any two stations follows a highly skewed power-law distribution.