• Title/Summary/Keyword: WNET

Search Result 2, Processing Time 0.017 seconds

A Comparison Study on the Weighted Network Centrality Measures of tnet and WNET (tnet과 WNET의 가중 네트워크 중심성 지수 비교 연구)

  • Lee, Jae Yun
    • Journal of the Korean Society for information Management
    • /
    • v.30 no.4
    • /
    • pp.241-264
    • /
    • 2013
  • This study compared and analyzed weighted network centrality measures supported by Opsahl's tnet and Lee's WNET, which are free softwares for weighted network analysis. Three node centrality measures including weighted degree, weighted closeness, and weighted betweenness are supported by tnet, and four node centrality measures including nearest neighbor centrality, mean association, mean profile association, triangle betweenness centrality are supported by WNET. An experimental analysis carried out on artificial network data showed tnet's high sensitiveness on linear transformations of link weights, however, WNET's centrality measures were insensitive to linear transformations. Seven centrality measures from both tools, tnet and WNET, were calculated on six real network datasets. The results showed the characteristics of weighted network centrality measures of tnet and WNET, and the relationships between them were also discussed.

Network Analysis of the Intellectual Structure of Addiction Research in Social Sciences: Based on the KCI Articles Published in 2019 (사회과학 중독연구 분야의 지적구조에 관한 네트워크 분석 : 2019년도 KCI 등재 논문을 기반으로)

  • Lee, Serim;Chun, JongSerl
    • The Journal of the Korea Contents Association
    • /
    • v.21 no.10
    • /
    • pp.21-37
    • /
    • 2021
  • This study investigated the intellectual structure of the latest trends in Korean addiction research in the social sciences. A network analysis of keywords with co-word occurrence was performed on 172 papers from the KCI database based on the data from the year of 2019, and a total of 432 keywords were extracted. The network analysis was performed using several programs: Bibexcel, COOC, WNET, and NodeXL. As a result of the study, keywords related to addiction type, study subjects, research methods, and research variables were found, and a total of 20 clusters were identified. Furthermore, to identify and measure weighted networks, the relationships between each keyword were explored and discussed in detail through a network analysis of global centralities, local centralities, and betweenness centralities. The study indicated that the latest issues were focused on smartphone addiction and provided implications for the future research and practice that fields and topics of relationship addiction, food addiction, and work addiction should be more considered. Further, the study discussed the relationship between drug addiction-crime, alcohol addiction-family, and gambling addiction-motivation and the necessity of qualitative study.