{"title":"一个klt启发的节点中心性，用于识别图中有影响的邻域","authors":"M. Ilyas, H. Radha","doi":"10.1109/CISS.2010.5464971","DOIUrl":null,"url":null,"abstract":"We present principal component centrality (PCC) as a measure of centrality that is more general and encompasses eigenvector centrality (EVC). We explain some of the difficulties in applying EVC to graphs and networks that contain more than just one neighborhood of nodes with high influence. We demonstrate the shortcomings of traditional EVC and contrast it against PCC. PCC's ranking procedure is based on spectral analysis of the network's graph adjacency matrix and identification of its most significant eigenvectors.","PeriodicalId":118872,"journal":{"name":"2010 44th Annual Conference on Information Sciences and Systems (CISS)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"A KLT-inspired node centrality for identifying influential neighborhoods in graphs\",\"authors\":\"M. Ilyas, H. Radha\",\"doi\":\"10.1109/CISS.2010.5464971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present principal component centrality (PCC) as a measure of centrality that is more general and encompasses eigenvector centrality (EVC). We explain some of the difficulties in applying EVC to graphs and networks that contain more than just one neighborhood of nodes with high influence. We demonstrate the shortcomings of traditional EVC and contrast it against PCC. PCC's ranking procedure is based on spectral analysis of the network's graph adjacency matrix and identification of its most significant eigenvectors.\",\"PeriodicalId\":118872,\"journal\":{\"name\":\"2010 44th Annual Conference on Information Sciences and Systems (CISS)\",\"volume\":\"120 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 44th Annual Conference on Information Sciences and Systems (CISS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISS.2010.5464971\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 44th Annual Conference on Information Sciences and Systems (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS.2010.5464971","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A KLT-inspired node centrality for identifying influential neighborhoods in graphs
We present principal component centrality (PCC) as a measure of centrality that is more general and encompasses eigenvector centrality (EVC). We explain some of the difficulties in applying EVC to graphs and networks that contain more than just one neighborhood of nodes with high influence. We demonstrate the shortcomings of traditional EVC and contrast it against PCC. PCC's ranking procedure is based on spectral analysis of the network's graph adjacency matrix and identification of its most significant eigenvectors.
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