• Title, Summary, Keyword: Betweenness Centrality

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Network Betweenness Centrality and Passenger Flow Analysis of Seoul Metropolitan Subway Lines (서울 수도권 지하철망의 호선별 망 매개 중심성과 승객 흐름 분석)

  • Lee, Kang Won;Lee, Jung Won
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.41 no.2
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    • pp.95-104
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    • 2018
  • Using network betweenness centrality we attempt to analyze the characteristics of Seoul metropolitan subway lines. Betweenness centrality highlights the importance of a node as a transfer point between any pairs of nodes. This 'transfer' characteristic is obviously of paramount importance in transit systems. For betweenness centrality, both traditional betweenness centrality measure and weighted betweenness centrality measure which uses monthly passenger flow amount between two stations are used. By comparing traditional and weighted betweenness centrality measures of lines characteristics of passenger flow can be identified. We also investigated factors which affect betweenness centrality. It is the number of passenger who get on or get off that significantly affects betweenness centrality measures. Through correlation analysis of the number of passenger and betweenness centrality, it is found out that Seoul metropolitan subway system is well designed in terms of regional distribution of population. Four measures are proposed which represent the passenger flow characteristics. It is shown they do not follow Power-law distribution, which means passenger flow is relatively evenly distributed among stations. It has been shown that the passenger flow characteristics of subway networks in other foreign cities such as Beijing, Boston and San Franciso do follow power-law distribution, that is, pretty much biased passenger flow traffic characteristics. In this study we have also tried to answer why passenger traffic flow of Seoul metropolitan subway network is more homogeneous compared to that of Beijing.

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.

The Impact of Influential's Betweenness Centrality on the WOM Effect under the Online Social Networking Service Environment (온라인 소셜 네트워크 서비스 환경에서 유력자의 매개 중심성이 구전 효과에 미치는 영향)

  • Park, Ji Hye;Suh, Bomil
    • Journal of Information Technology Applications and Management
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    • v.20 no.2
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    • pp.127-146
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    • 2013
  • The online social networking services (SNS) have been growing as the means of communication. In this study, we investigated word-of-mouth (WOM) effect under the SNS environment and evaluated the impact of message sender's influence on the WOM effect. Especially, this study focused on the betweenness centrality calculated through the social network analysis (SNA) of SNS network information, and proposed it as the measure of WOM message sender's influence, SNA may provide more accurate and objective measures than subjective self-reporting survey method. Fifty-one Facebook users responded to each of their four Facebook friends, who had been selected based on their betweenness centrality, Statistical analyses were performed using the responses and the betweenness centralities of the Facebook friends. The results showed that the direction (positive vs, negative) of a WOM message in SNS had an impact on the attitude of the message receiver toward the product. Moreover, the betweenness centrality of the message sender as well as his/her opinion leadership had a moderating effect on the WOM effect. Opinion leadership is a measure that has been frequently used for indicating the influence of WOM message sender in the previous studies. Considering the result that the betweenness centrality of the message sender was Significantly correlated to his/her opinion leadership, the betweenness centrality can be used for indicating the influence of WOM message sender.

An Efficient Algorithm for Betweenness Centrality Estimation in Social Networks (사회관계망에서 매개 중심도 추정을 위한 효율적인 알고리즘)

  • Shin, Soo-Jin;Kim, Yong-Hwan;Kim, Chan-Myung;Han, Youn-Hee
    • KIPS Transactions on Software and Data Engineering
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    • v.4 no.1
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    • pp.37-44
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    • 2015
  • In traditional social network analysis, the betweenness centrality measure has been heavily used to identify the relative importance of nodes. Since the time complexity to calculate the betweenness centrality is very high, however, it is difficult to get it of each node in large-scale social network where there are so many nodes and edges. In our past study, we defined a new type of network, called the expanded ego network, which is built only with each node's local information, i.e., neighbor information of the node's neighbor nodes, and also defined a new measure, called the expanded ego betweenness centrality. In this paper, We propose algorithm that quickly computes expanded ego betweenness centrality by exploiting structural properties of expanded ego network. Through the experiment with virtual network used Barab$\acute{a}$si-Albert network model to represent the generic social network and facebook network to represent actual social network, We show that the node's importance rank based on the expanded ego betweenness centrality has high similarity with that the node's importance rank based on the existing betweenness centrality. We also show that the proposed algorithm computes the expanded ego betweenness centrality quickly than existing algorithm.

Local Information-based Betweenness Centrality to Identify Important Nodes in Social Networks (사회관계망에서 중요 노드 식별을 위한 지역정보 기반 매개 중심도)

  • Shon, Jin Gon;Kim, Yong-Hwan;Han, Youn-Hee
    • KIPS Transactions on Computer and Communication Systems
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    • v.2 no.5
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    • pp.209-216
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    • 2013
  • In traditional social network analysis, the betweenness centrality measure has been heavily used to identify the relative importance of nodes in terms of message delivery. Since the time complexity to calculate the betweenness centrality is very high, however, it is difficult to get it of each node in large-scale social network where there are so many nodes and edges. In this paper, we define a new type of network, called the expanded ego network, which is built only with each node's local information, i.e., neighbor information of the node's neighbor nodes, and also define a new measure, called the expended ego betweenness centrality. Through the intensive experiment with Barab$\acute{a}$si-Albert network model to generate the scale-free networks which most social networks have as their embedded feature, we also show that the nodes' importance rank based on the expanded ego betweenness centrality has high similarity with that based on the traditional betweenness centrality.

A Study on the Application to Network Analysis on the Importance of Author Keyword based on the Position of Keyword (학술논문의 저자키워드 출현순서에 따른 저자키워드 중요도 측정을 위한 네트워크 분석방법의 적용에 관한 연구)

  • Kwon, Sun-Young
    • Journal of the Korean Society for information Management
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    • v.31 no.2
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    • pp.121-142
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    • 2014
  • This study aims to investigate the importance of author keyword with analysis the position of author keyword in journal. In the first stage, an analysis was carried out on the position of author keyword. We examined the importance of author keyword by using degree centrality, closeness centrality, betweenness centrality, eigenvector centrality and effective size of structural hole. In the next stage, We performed analysis on correlation between network centrality measures and the position of author keyword. The result of correlation analysis on network centrality measures and the position of author keyword shows that there are the more significant areas of the result of the correlation analysis on degree centrality, betweenness centrality and the position of keyword. In addition, These results show that we need to consider that the possible way as measuring the importance of author keyword in journal is not only a term frequency but also degree centrality and betweenness centrality.

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.

Revealing the Structure of National R & D Activity in Korea by Using a Keyword Co-occurrence Network

  • Ahn, Min-Woo;Jung, Woo-Sung
    • New Physics: Sae Mulli
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    • v.67 no.5
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    • pp.581-587
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    • 2017
  • R & D activity is crucial for economic growth of modern society. For this reason, government supports R & D activity in various ways. In this paper, we investigate the structure of the keyword co-occurrence network of national R & D activity from the National Science & Technology Information System (NTIS) database from 2006 to 2010. In this network, a keyword is a node, and two nodes are connected when they are included in the same research project. Using investment data, we measure the investment for each keyword and investigate the relation with the centrality of keywords for the constructed network. We obtain three major findings from the keyword co-occurrence networks. First, a heavy-tailed degree distribution with a hierarchical structure is observed. They are modularized with similar classification profiles. Second, betweenness centrality and investment both show heavy-tailed distributions. However, they are not only positively correlates rather, two kinds of relationships are observed. One is the keyword that has both high centrality and investment, and the other keyword has low centrality, but high investment. Third, investment and centrality show different temporal consistencies. When we measure the Pearson correlation coefficient of investment and centrality for adjacent years, investment changes more rapidly than the betweenness centrality does.

Study on Influence and Diffusion of Word-of-Mouth in Online Fashion Community Network (온라인 패션커뮤니티 네트워크에서의 구전 영향력과 확산력에 관한 연구)

  • Song, Kieun;Lee, Duk Hee
    • Journal of the Korean Society of Costume
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    • v.65 no.6
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    • pp.25-35
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    • 2015
  • The purpose of this study is to investigate the characteristics of members and communities that have significant influence in the online fashion community through their word-of-mouth activities. In order to identify the influence and the diffusion of word-of-mouth in fashion community, the study selected one online fashion community. Then, the study sorted the online posts and comments made on fashion information and put them into the matrix form to perform social network analysis. The result of the analysis is as follows: First, the fashion community network used in the study has many active members that relay information very quickly. Average time for information diffusion is very short, taking only one or two days in most cases. Second, the influence of word-of-mouth is led by key information produced from only a few members. The number of influential members account for less than 20% of the total number of community members, which indicate high level of degree centrality. The diffusion of word-of-mouth is led by even fewer members, which represent high level of betweenness centrality, compared to the case of degree centrality. Third, component characteristic shares similar information with about 70% of all members being linked to maximize information influence and diffusion. Fourth, a node with high degree centrality and betweenness centrality shares similar interests, presenting strain effect to particular information. Specially, members with high betweenness centrality show similar interests with members of high degree centrality. The members with high betweenness centrality also help expansion of related information by actively commenting on posts. The result of this research emphasizes the necessity of creation and management of network to efficiently convey fashion information by identifying key members with high level of information influence and diffusion to enhance the outcome of online word-of-mouth.

A Comparative Study on the Centrality Measures for Analyzing Research Collaboration Networks (공동연구 네트워크 분석을 위한 중심성 지수에 대한 비교 연구)

  • Lee, Jae Yun
    • Journal of the Korean Society for information Management
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    • v.31 no.3
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    • pp.153-179
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    • 2014
  • This study explores the characteristics of centrality measures for analyzing researchers' impact and structural positions in research collaboration networks. We investigate four binary network centrality measures (degree centrality, closeness centrality, betweenness centrality, and PageRank), and seven existing weighted network centrality measures (triangle betweenness centrality, mean association, weighted PageRank, collaboration h-index, collaboration hs-index, complex degree centrality, and c-index) for research collaboration networks. And we propose SSR, which is a new weighted centrality measure for collaboration networks. Using research collaboration data from three different research domains including architecture, library and information science, and marketing, the above twelve centrality measures are calculated and compared each other. Results indicate that the weighted network centrality measures are needed to consider collaboration strength as well as collaboration range in research collaboration networks. We also recommend that when considering both collaboration strength and range, it is appropriate to apply triangle betweenness centrality and SSR to investigate global centrality and local centrality in collaboration networks.