Network Betweenness Centrality and Passenger Flow Analysis of Seoul Metropolitan Subway Lines

Kang Won Lee(이강원),Jung Won Lee(이정원) 한국산업경영시스템학회 2018 한국산업경영시스템학회지 Vol.41 No.2

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.

서울 수도권 지하철망의 호선별 망 매개 중심성과 승객 흐름 분석

이강원,이정원 한국산업경영시스템학회 2018 한국산업경영시스템학회지 Vol.41 No.2

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.

사회관계망에서 매개 중심도 추정을 위한 효율적인 알고리즘

신수진 ( Soo Jin Shin ),김용환 ( Yong Hwan Kim ),김찬명 ( Chan Myung Kim ),한연희 ( Youn Hee Han ) 한국정보처리학회 2015 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.4 No.1

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 Barabasi-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.

Trade Competence Reinforcement Strategywith Trade Network Analysis: Focused on ASEAN and KOREA

Chunsu Lee 한국무역금융보험학회(구 한국무역보험학회) 2020 무역금융보험연구 Vol.21 No.3

본 연구는 ASEAN 10개국과 한국에 대한 네트워크 중심성 분석을 통하여 다음과 같은 결과를 얻었다. 교역량 증대로 무역의 발전 가능성 및 경쟁력을 내포하고 있는 연결정도 중심성(DC : Degree Centrality)은 수출에 있어서 싱가포르, 한국, 말레이시아 순으로 높게 나타났다. 또한 수입에 있어서는 싱가포르, 베트남, 한국 순으로 높게 나타났다. 무역에 있어서 가장 빨리 국제 환경에 적응하고 대비하는 순간적응력인 근접 중심성(CC : Closeness Centrality)을 보면, 수출 과 수입 면에서 캄보디아, 인도네시아, 말레이시아, 필리핀, 싱가포르, 태국, 베트남, 한국이 모 두 비슷한 수준을 보였다. 그러나 상대적으로 미얀마, 브루나이, 라오스가 무역환경 적응과 대 비가 약한 것으로 보여주고 있다. 무역 흐름의 통제력을 나타내는 매개 중심성(BC : Betweenness Centrality)을 통해 중심국과 주 변국과의 영향력을 실증 및 시각적으로 분석한 결과를 보면, 수입과 수출에서 비슷한 결과를 보여주고 있다. 수출에 있어서 말레이시아, 싱가포르, 태국, 베트남, 한국은 수출매개 중심성이 비슷한 높은 그룹 국가에 속했다. 그리고 상대적으로 수출/수입 무역 영향력이 캄보디아, 인도 네시아는 약한 것으로 나타났다. 또한 브루나이, 라오스, 미얀마는 매우 약한 것으로 나타났다. 이러한 실증적인 네트워크 중심성 분석을 토대로 한국과 아세안 국가들에 대한 선택과 집중을 활용할 수 있는 방안을 모색 할 수 있다. 즉, 아세안 국가 중 중심성이 높은 국가를 파악하고 이를 선택하여 우선적으로 공략할 국가를 파악할 수 있다. 또한 중심성이 낮은 국가들은 다양 하고 적절한 대안을 모색할 수 있을 것이다. Purpose : This study identified important countries for ASEAN and Korea through social network analysis in which trade strategies can be selectively and intensively applied. So, This study academically identified countries with high centrality for trade expansion. and this study can be used as an efficient and policy analysis data for successful trade insurance with ASEAN countries having high centrality by applying a systematic and analytical social network methodology. Research design, data, methodology : In this study, ASEAN nodes (countries) were obtained from statistical data in Export/Import amounts from the International Trade Center (ITC). Import and export data matrix was made for 10 ASEAN countries for year 2017 and was utilized for network analysis. NetMiner program was used to analyze degree, closeness and betweenness centrality. Results : The network centrality analysis of 10 ASEAN countries and Korea in this study produced following results. In terms of degree centrality, which implies the possibility of trade development and competitiveness through increasing trade volume, Singapore, Republic of Korea and Malaysia had the highest centrality in exports in descending order. In imports, Singapore had the highest centrality and Vietnam and Republic of Korea in descending order. Closeness centrality, which reflects the instantaneous adaptation to prepare for and fast adaptation to the international environment in trade. Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand, Vietnam and Korea all showed similar levels of export and import closeness centrality. However, Myanmar, Brunei and Laos showed low levels of closeness centrality. In empirical and visual analysis of central nation and its neighboring nations through the betweenness centrality, which represents the control of the trade flow, showed similar results for both imports and exports. Malaysia, Singapore, Thailand, Vietnam and Korea were among the high group countries with similar export betweenness centralities. Cambodia and Indonesia seemed were relatively low in export betweenness centrality. In addition, Brunei, Laos and Myanmar were found to be very low in export betweenness centrality. Conclusions : Based on the results of this empirical network analysis, it will be possible to seek a way to utilize choice and concentration strategy for Korea and ASEAN countries. In other words, countries with high centrality can be identified and then country choice can be made to target first. As for the economic and social expectations of this study, Korea can diversity trade risks and share investment on countries with high centrality and consequently enhancing Korean trade competitiveness.

사회연결망 분석을 이용한 항만경제학 분야 공동연구의 중심성에 관한 연구

손용정 ( Son Yong-jung ) 한국도서(섬)학회 2017 韓國島嶼硏究 Vol.29 No.1

Increased joint research means increase in exchange of knowledge and academic information between scholars. Social network analysis is used to analyse the meaning of increased communication between scholars and complex relations between structural characteristics, and whether they have an effect on ones academic activity. Collaboration network is a product of social cooperation formed by researchers. Academic cooperation network can be the necessary solution to convergent themes and long-term assignments. This study analysed the structural characteristics of a co-author network as an academic community in port economics through co-author data. For this study, a total of 248 papers published recently(between 2011 and 2105) were analysed. As for the analysis of research centrality of co-authors in port economics, a network using joint research structure was visualized. Port economics has inter-disciplinary networks connecting many researchers in a number of small-scale networks. The results are as follows: First, the degree of centrality for standardized actor Gi-Tae Yeo was 6.048%, which was the largest. He has a network with another 15 researchers. Second, the standard betweenness centrality of Gi-Tae Yeo was largest recording 0.546%. Third, the largest value for eigenvector centrality developed by Gi-Tae Yeo was 74.0735%. Geon-Sik Jo, Sung-Bum Kim, Jae-Young Kim, Nam-Yeon Lee, Sung-Jae Yoo, Jin-Hang Cho, Ho-Young Lee and Sung-il Park were relatively low in their centrality degree while they were close to the top in eigenvector centrality.

학술논문의 저자키워드 출현순서에 따른 저자키워드 중요도 측정을 위한 네트워크 분석방법의 적용에 관한 연구

권선영 한국정보관리학회 2014 정보관리학회지 Vol.31 No.2

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. 본 연구는 학술논문의 저자키워드 출현순서에 따른 저자키워드의 중요도를 측정해보고자 하는 연구이다. 먼저 출현순서에 따른 저자키워드의 특징을 분석한 후 네트워크 분석 방법의 연결정도중심성, 근접중심성, 매개중심성, 위세중심성, 그리고 네트워크의 구조적공백성의 효과크기와 같은 지수를 사용하여 학술논문의 저자키워드 출현순서에 따른 저자키워드의 중요도를 측정해보았으며 각각의 네트워크 지수와 저자키워드의 출현순서와의 상관관계분석을 수행하였다. 네트워크 분석 지수 중 연결정도중심성 지수, 매개중심성 지수의 경우 각 학문분야별 저자키워드의 출현순서와의 상관관계의 결과에서의 유의한 분야의 수가 비교적 다른 지수에 비해 많았다. 이와 같은 결과를 통해 저자키워드의 중요도를 단지 출현빈도만으로 판단했던 것에서 벗어나 저자키워드의 중요도 측정을 위한 방법으로 연결정도중심성 지수, 매개중심성 지수도 고려해 볼 수 있음을 알 수 있었다.

A Study on the Research Trends of Big Data at Public Libraries : with a Focus on the Journal “Public Library Quarterly”

이성신,김현숙,백수민,윤수빈 건국대학교 GLOCAL(글로컬)캠퍼스 지식콘텐츠연구소 2022 International Journal of Knowledge Content Develop Vol.12 No.-

This study aims to analyze the Big Data-related research trends in thefield of public library by using the social network analytical method. One hundred seventeen articles published in the journal ‘Public Library Quarterly’ were analyzed with author keywords and the frequency, degree centrality, while the betweenness centrality of the keywords were examined. The keywords “programs” and “community development” demonstrated the highest degree of centrality. The keywords “programs”, “strategic planning”, “community development”, “future of libraries”, “outreach”, and “evaluation” posted a high degree of centrality and betweenness centrality. The keywords “measurement”, “survey”, “community partnerships”, and “community engagement” demonstrated a high degree of centrality but not a high betweenness centrality. Meanwhile, the keywords “planning”, “marketing”, “community needs”, and “community building” demonstrated a high betweenness centrality but not a high degree centrality. Based on the results of this study, public libraries should focus on the following directions of research when focusing on the Big Data. First, as mentioned in the above, a more in-depth discussion is needed regarding COVID-19 and social media. Second, the academic interest in the Big Data related education and training for the public librarians and educational contents is needed. Third, public libraries should think about the ways to efficiently perform their roles as a local data center, including their cooperation with other organizations in their local community.

SNS에서의 개선된 소셜 네트워크 분석 방법

손종수(Jong-Soo Sohn),조수환(Soo-Whan Cho),권경락(Kyung-Lag Kwon),정인정(In-Jeong Chung) 한국지능정보시스템학회 2012 지능정보연구 Vol.18 No.4

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and brea

사회연결망분석을 이용한 여자필드하키 국가 간 팀워크 검증

이희화,김지응 한국스포츠학회 2019 한국스포츠학회지 Vol.17 No.2

본 연구는 사회연결망 분석을 활용하여 리우올림픽에 참가한 대한민국 필드하키 대표팀과 상위 4개국의 경기패 턴 및 팀워크를 확인하는데 목적이 있다. 연구대상은 리우올림픽에 참가한 대한민국, 뉴질랜드, 독일, 네덜란드, 영국 여 자하키대표팀 이며, 자료처리 방법으로는 올림픽 하키 영상을 바탕으로 스포츠코드를 활용하여 서클진입에 연관된 선 수데이터를 입력하였다. 처리된 자료는 대칭형매트릭스를 만들어 Ucinet6를 활용하여 연결중심성, 사이중심성을 분석 을 실시하였으며, 국가별 차이 검증을 위하여 SPSS21.0의 ANOVA을 실시하였다. 연구결과는 대한민국은 다른 국가 에 비하여 연결중심성은 가장 낮게 나타났으며, 사이중심성은 가장 높게 나타났다. 또한 연결중심성 및 사이중심성 차이 에 대한 집단별 검증에서도 대한민국, 영국과 독일은 역습 위주의 경기패턴을 네덜란드와 뉴질랜드는 많은 패스를 활용 한 서클진입 공격을 전개한 것으로 나타났다. The purpose of this study is to identify the game patterns and teamwork of the Korean field hockey team and the top four countries participating in the Rio Olympics by utilizing the analysis of social networks. The research target is the women’s hockey team of South Korea, New Zealand, Germany, the Netherlands and the U.K., which participated in the Rio Olympics. As a data processing method, athletes’ data related to circle entry were entered using sports code based on Olympic hockey images. The processed data were analyzed for Degree-Centrality and Betweenness-Centrality using Ucinet 6 by creating symmetrical matrix and ANOVA of SPSS21.0 was performed for verification of differences by country. The results of the research show that South Korea had the lowest Degree-Centrality compared to other countries, and that it had the highest level of Betweenness-Centrality. In addition, group-by-group verification of the difference between the Degree-Centrality and the Betweenness-Centrality showed that South Korea, the U.K. and Germany had counterattack-oriented game patterns, while the Netherlands and New Zealand conducted circle entry attacks using many passes.

韓國經濟의 네트워크구조변화에 관한 연구

조상섭(Sang Sup Cho),박종찬(Jongchan Park) 한국산업경제학회 2014 산업경제연구 Vol.27 No.6

본 연구목적은 2000년도부터 2010년도까지 우리나라 경제구조변화를 네트워크접근방법에 의하여 분석하는 데 있다. 2000년도와 2010년도의 실측 산업연관표를 이용하여 우리나라 산업의 공급충격에 대한 민감도(Random Walking Centrality)와 지속도(Counting Betweenness)를 분석하였다. 본 분석결과를 간단하게 요약하면 다음과 같다. 첫째, 우리나라 경제구조변화는 충격 민감도증가와 충격 지속도감소로 대변될 수 있다. 둘째, 충격 민감도에 따른 네트워크 중심성변화추이는 2000년도에 전력.가스.수도 산업에서 2010년도에 건설업으로 이동하였다. 셋째, 충격 지속도에 따른 네트워크중심성변화는 역시 2000년도에 전력.가스.수도 산업에서 2010년도에 건설업으로 이동하였다. 마지막으로 중심성에 기초한 네트워크방법론에 따른 핵심 산업과 기존에 사용하고 있는 전방 및 후방연관효과 의한 핵심 산업이 서로 다르게 나타났다. 상기 분석결과에 대한 시사점으로 우리나라 경제에 대한 네트워크구조변화의 원인으로 2000대 전반기 경제 인프라구축중심에서 2000년대 후반에 건설 산업중심의 정책추진패러다임변화가 우리나라 산업구조변화에 빠르게 반영된 것으로 볼 수 있겠다. The study is analyze the our economy structure change using network method during 2000 through 2010. Using 2000 and 2010 input output tables, we study random walking centrality and counting betweenness under exogenous shocks. Our results is to show that first, Korea economy has experienced the increase of random walking centrality and the decrease of counting betweenness. Second, according to random walking centrality, the centrality has changed from electricity, gas and utility industry to construction industry. Third, according to counting betweenness, the centrality has changed from electricity, gas and utility industry to construction industry as well. Finally, there is the different results between traditional linkage analysis and network analysis for selecting of key industry. We imply that the empirical results reflect policy paradigm change from economic infrastructure pursuit to construction business pursuit.