Robert Pienta, Acar Tamersoy, Hanghang Tong, Duen Horng Chau
{"title":"MAGE:丰富属性图中的近似模式匹配。","authors":"Robert Pienta, Acar Tamersoy, Hanghang Tong, Duen Horng Chau","doi":"10.1109/BigData.2014.7004278","DOIUrl":null,"url":null,"abstract":"<p><p>Given a large graph with millions of nodes and edges, say a social network where both its nodes and edges have multiple attributes (e.g., job titles, tie strengths), how to quickly find subgraphs of interest (e.g., a ring of businessmen with strong ties)? We present MAGE, a scalable, multicore subgraph matching approach that supports expressive queries over large, richly-attributed graphs. Our major contributions include: (1) MAGE supports graphs with both node and edge attributes (most existing approaches handle either one, but not both); (2) it supports expressive queries, allowing multiple attributes on an edge, wildcards as attribute values (i.e., match <i>any</i> permissible values), and attributes with continuous values; and (3) it is scalable, supporting graphs with several hundred million edges. We demonstrate MAGE's effectiveness and scalability via extensive experiments on large real and synthetic graphs, such as a Google+ social network with <i>460 million</i> edges.</p>","PeriodicalId":74501,"journal":{"name":"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data","volume":"2014 ","pages":"585-590"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4388251/pdf/nihms675787.pdf","citationCount":"0","resultStr":"{\"title\":\"MAGE: Matching Approximate Patterns in Richly-Attributed Graphs.\",\"authors\":\"Robert Pienta, Acar Tamersoy, Hanghang Tong, Duen Horng Chau\",\"doi\":\"10.1109/BigData.2014.7004278\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Given a large graph with millions of nodes and edges, say a social network where both its nodes and edges have multiple attributes (e.g., job titles, tie strengths), how to quickly find subgraphs of interest (e.g., a ring of businessmen with strong ties)? We present MAGE, a scalable, multicore subgraph matching approach that supports expressive queries over large, richly-attributed graphs. Our major contributions include: (1) MAGE supports graphs with both node and edge attributes (most existing approaches handle either one, but not both); (2) it supports expressive queries, allowing multiple attributes on an edge, wildcards as attribute values (i.e., match <i>any</i> permissible values), and attributes with continuous values; and (3) it is scalable, supporting graphs with several hundred million edges. We demonstrate MAGE's effectiveness and scalability via extensive experiments on large real and synthetic graphs, such as a Google+ social network with <i>460 million</i> edges.</p>\",\"PeriodicalId\":74501,\"journal\":{\"name\":\"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data\",\"volume\":\"2014 \",\"pages\":\"585-590\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4388251/pdf/nihms675787.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BigData.2014.7004278\",\"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 : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BigData.2014.7004278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MAGE: Matching Approximate Patterns in Richly-Attributed Graphs.
Given a large graph with millions of nodes and edges, say a social network where both its nodes and edges have multiple attributes (e.g., job titles, tie strengths), how to quickly find subgraphs of interest (e.g., a ring of businessmen with strong ties)? We present MAGE, a scalable, multicore subgraph matching approach that supports expressive queries over large, richly-attributed graphs. Our major contributions include: (1) MAGE supports graphs with both node and edge attributes (most existing approaches handle either one, but not both); (2) it supports expressive queries, allowing multiple attributes on an edge, wildcards as attribute values (i.e., match any permissible values), and attributes with continuous values; and (3) it is scalable, supporting graphs with several hundred million edges. We demonstrate MAGE's effectiveness and scalability via extensive experiments on large real and synthetic graphs, such as a Google+ social network with 460 million edges.