{"title":"简并如何帮助图流中的三角形计数","authors":"Suman Kalyan Bera, Seshadhri Comandur","doi":"10.1145/3375395.3387665","DOIUrl":null,"url":null,"abstract":"We revisit the well-studied problem of triangle count estimation in graph streams. Given a graph represented as a stream of m edges, our aim is to compute a (1+-ε)-approximation to the triangle count T, using a small space algorithm. For arbitrary order and a constant number of passes, the space complexity is known to be essentially Θ(min(m3/2 /T, m/√T)) (McGregor et al., PODS 2016, Bera et al., STACS 2017). We give a (constant pass, arbitrary order) streaming algorithm that can circumvent this lower bound for low degeneracy graphs. The degeneracy, K, is a nuanced measure of density, and the class of constant degeneracy graphs is immensely rich (containing planar graphs, minor-closed families, and preferential attachment graphs). We design a streaming algorithm with space complexity ~O(mK/T). For constant degeneracy graphs, this bound is ~O(m/T), which is significantly smaller than both m3/2 /T and m/√T. We complement our algorithmic result with a nearly matching lower bound of Ω(mK/T).","PeriodicalId":412441,"journal":{"name":"Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"How the Degeneracy Helps for Triangle Counting in Graph Streams\",\"authors\":\"Suman Kalyan Bera, Seshadhri Comandur\",\"doi\":\"10.1145/3375395.3387665\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We revisit the well-studied problem of triangle count estimation in graph streams. Given a graph represented as a stream of m edges, our aim is to compute a (1+-ε)-approximation to the triangle count T, using a small space algorithm. For arbitrary order and a constant number of passes, the space complexity is known to be essentially Θ(min(m3/2 /T, m/√T)) (McGregor et al., PODS 2016, Bera et al., STACS 2017). We give a (constant pass, arbitrary order) streaming algorithm that can circumvent this lower bound for low degeneracy graphs. The degeneracy, K, is a nuanced measure of density, and the class of constant degeneracy graphs is immensely rich (containing planar graphs, minor-closed families, and preferential attachment graphs). We design a streaming algorithm with space complexity ~O(mK/T). For constant degeneracy graphs, this bound is ~O(m/T), which is significantly smaller than both m3/2 /T and m/√T. We complement our algorithmic result with a nearly matching lower bound of Ω(mK/T).\",\"PeriodicalId\":412441,\"journal\":{\"name\":\"Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3375395.3387665\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3375395.3387665","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How the Degeneracy Helps for Triangle Counting in Graph Streams
We revisit the well-studied problem of triangle count estimation in graph streams. Given a graph represented as a stream of m edges, our aim is to compute a (1+-ε)-approximation to the triangle count T, using a small space algorithm. For arbitrary order and a constant number of passes, the space complexity is known to be essentially Θ(min(m3/2 /T, m/√T)) (McGregor et al., PODS 2016, Bera et al., STACS 2017). We give a (constant pass, arbitrary order) streaming algorithm that can circumvent this lower bound for low degeneracy graphs. The degeneracy, K, is a nuanced measure of density, and the class of constant degeneracy graphs is immensely rich (containing planar graphs, minor-closed families, and preferential attachment graphs). We design a streaming algorithm with space complexity ~O(mK/T). For constant degeneracy graphs, this bound is ~O(m/T), which is significantly smaller than both m3/2 /T and m/√T. We complement our algorithmic result with a nearly matching lower bound of Ω(mK/T).