{"title":"确定性图行走程序挖掘","authors":"Peter Belcák, Roger Wattenhofer","doi":"10.48550/arXiv.2208.10290","DOIUrl":null,"url":null,"abstract":". Owing to their versatility, graph structures admit representations of intricate relationships between the separate entities compris-ing the data. We formalise the notion of connection between two vertex sets in terms of edge and vertex features by introducing graph-walking programs. We give two algorithms for mining of deterministic graph-walking programs that yield programs in the order of increasing length. These programs characterise linear long-distance relationships between the given two vertex sets in the context of the whole graph.","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"130 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deterministic Graph-Walking Program Mining\",\"authors\":\"Peter Belcák, Roger Wattenhofer\",\"doi\":\"10.48550/arXiv.2208.10290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". Owing to their versatility, graph structures admit representations of intricate relationships between the separate entities compris-ing the data. We formalise the notion of connection between two vertex sets in terms of edge and vertex features by introducing graph-walking programs. We give two algorithms for mining of deterministic graph-walking programs that yield programs in the order of increasing length. These programs characterise linear long-distance relationships between the given two vertex sets in the context of the whole graph.\",\"PeriodicalId\":244337,\"journal\":{\"name\":\"International Conference on Advanced Data Mining and Applications\",\"volume\":\"130 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Advanced Data Mining and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48550/arXiv.2208.10290\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Advanced Data Mining and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2208.10290","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
. Owing to their versatility, graph structures admit representations of intricate relationships between the separate entities compris-ing the data. We formalise the notion of connection between two vertex sets in terms of edge and vertex features by introducing graph-walking programs. We give two algorithms for mining of deterministic graph-walking programs that yield programs in the order of increasing length. These programs characterise linear long-distance relationships between the given two vertex sets in the context of the whole graph.