{"title":"复杂网络的并行封闭中心性算法","authors":"K. Erciyes","doi":"10.1109/iisec54230.2021.9672334","DOIUrl":null,"url":null,"abstract":"Complex networks are large and analysis of these networks require significantly different methods than small networks. Parallel processing is needed to provide analysis of these networks in a timely manner. Graph centrality measures provide convenient methods to assess the structure of these networks. We review main centrality algorithms, describe implementation of closed centrality in Python and propose a simple parallel algorithm of closed centrality and show its implementation in Python with obtained results.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A parallel closed centrality algorithm for complex networks\",\"authors\":\"K. Erciyes\",\"doi\":\"10.1109/iisec54230.2021.9672334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Complex networks are large and analysis of these networks require significantly different methods than small networks. Parallel processing is needed to provide analysis of these networks in a timely manner. Graph centrality measures provide convenient methods to assess the structure of these networks. We review main centrality algorithms, describe implementation of closed centrality in Python and propose a simple parallel algorithm of closed centrality and show its implementation in Python with obtained results.\",\"PeriodicalId\":344273,\"journal\":{\"name\":\"2021 2nd International Informatics and Software Engineering Conference (IISEC)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd International Informatics and Software Engineering Conference (IISEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iisec54230.2021.9672334\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iisec54230.2021.9672334","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A parallel closed centrality algorithm for complex networks
Complex networks are large and analysis of these networks require significantly different methods than small networks. Parallel processing is needed to provide analysis of these networks in a timely manner. Graph centrality measures provide convenient methods to assess the structure of these networks. We review main centrality algorithms, describe implementation of closed centrality in Python and propose a simple parallel algorithm of closed centrality and show its implementation in Python with obtained results.