{"title":"可视化社会网络数据:亚裔美国学生会议的比较研究","authors":"R. Palmieri, C. Giglio","doi":"10.1109/FiCloud.2015.100","DOIUrl":null,"url":null,"abstract":"In this paper we propose a comparative study of social network data related to three Asian-American student conferences: Taiwan-America Student Conference (TASC), Japan-America Student Conference (JASC) and Korea-America Student Conference (KASC). Such a study is built on the literature review of existing visualization methods and is based on the adoption of open source and freely available tools for Social Network Analysis (SNA). The main aim of this work is that of analyzing and comparing interaction patterns and sub-networks dynamics of attending students emerging from the collected data. In particular, data have been extracted in wide temporal horizons starting 30 days before and finishing 30 days after the application deadlines.","PeriodicalId":182204,"journal":{"name":"2015 3rd International Conference on Future Internet of Things and Cloud","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Visualizing Social Network Data: A Comparative Study of Asian-American Student Conferences\",\"authors\":\"R. Palmieri, C. Giglio\",\"doi\":\"10.1109/FiCloud.2015.100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a comparative study of social network data related to three Asian-American student conferences: Taiwan-America Student Conference (TASC), Japan-America Student Conference (JASC) and Korea-America Student Conference (KASC). Such a study is built on the literature review of existing visualization methods and is based on the adoption of open source and freely available tools for Social Network Analysis (SNA). The main aim of this work is that of analyzing and comparing interaction patterns and sub-networks dynamics of attending students emerging from the collected data. In particular, data have been extracted in wide temporal horizons starting 30 days before and finishing 30 days after the application deadlines.\",\"PeriodicalId\":182204,\"journal\":{\"name\":\"2015 3rd International Conference on Future Internet of Things and Cloud\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 3rd International Conference on Future Internet of Things and Cloud\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FiCloud.2015.100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 3rd International Conference on Future Internet of Things and Cloud","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FiCloud.2015.100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visualizing Social Network Data: A Comparative Study of Asian-American Student Conferences
In this paper we propose a comparative study of social network data related to three Asian-American student conferences: Taiwan-America Student Conference (TASC), Japan-America Student Conference (JASC) and Korea-America Student Conference (KASC). Such a study is built on the literature review of existing visualization methods and is based on the adoption of open source and freely available tools for Social Network Analysis (SNA). The main aim of this work is that of analyzing and comparing interaction patterns and sub-networks dynamics of attending students emerging from the collected data. In particular, data have been extracted in wide temporal horizons starting 30 days before and finishing 30 days after the application deadlines.
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