{"id":"https://openalex.org/W2143211346","doi":"https://doi.org/10.14778/1687627.1687710","title":"Output space sampling for graph patterns","display_name":"Output space sampling for graph patterns","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W2143211346","doi":"https://doi.org/10.14778/1687627.1687710","mag":"2143211346"},"language":"en","primary_location":{"id":"doi:10.14778/1687627.1687710","is_oa":false,"landing_page_url":"https://doi.org/10.14778/1687627.1687710","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044451399","display_name":"Mohammad Al Hasan","orcid":"https://orcid.org/0000-0002-8279-1023"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mohammad Al Hasan","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, NY"],"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, NY","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019559411","display_name":"Mohammed J. Zaki","orcid":"https://orcid.org/0000-0003-4711-0234"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammed J. Zaki","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, NY"],"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, NY","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044451399"],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":20.9179,"has_fulltext":false,"cited_by_count":143,"citation_normalized_percentile":{"value":0.99196698,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"2","issue":"1","first_page":"730","last_page":"741"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7681213617324829},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.661389946937561},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6601841449737549},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6407550573348999},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6082873344421387},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4962225556373596},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.46373167634010315},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.45840245485305786},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4447484612464905},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4317851662635803},{"id":"https://openalex.org/keywords/sample-space","display_name":"Sample space","score":0.41154107451438904},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.292815625667572}],"concepts":[{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7681213617324829},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.661389946937561},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6601841449737549},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6407550573348999},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6082873344421387},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4962225556373596},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.46373167634010315},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.45840245485305786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4447484612464905},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4317851662635803},{"id":"https://openalex.org/C100279318","wikidata":"https://www.wikidata.org/wiki/Q467440","display_name":"Sample space","level":2,"score":0.41154107451438904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.292815625667572},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.14778/1687627.1687710","is_oa":false,"landing_page_url":"https://doi.org/10.14778/1687627.1687710","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.150.9531","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.9531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.rpi.edu/~zaki/PaperDir/VLDB09.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.151.3813","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.3813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.vldb.org/pvldb/2/vldb09-713.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W25964860","https://openalex.org/W1524882660","https://openalex.org/W1568495775","https://openalex.org/W1578099820","https://openalex.org/W1597561788","https://openalex.org/W1607305617","https://openalex.org/W1970259423","https://openalex.org/W1985104873","https://openalex.org/W1985408389","https://openalex.org/W1999848435","https://openalex.org/W2028944004","https://openalex.org/W2029817244","https://openalex.org/W2033370508","https://openalex.org/W2062277303","https://openalex.org/W2066277072","https://openalex.org/W2070378998","https://openalex.org/W2086148106","https://openalex.org/W2089211037","https://openalex.org/W2100061586","https://openalex.org/W2106072141","https://openalex.org/W2108150886","https://openalex.org/W2109722477","https://openalex.org/W2115364986","https://openalex.org/W2116029313","https://openalex.org/W2117848926","https://openalex.org/W2118349699","https://openalex.org/W2124833098","https://openalex.org/W2126359798","https://openalex.org/W2131374321","https://openalex.org/W2136593687","https://openalex.org/W2146774885","https://openalex.org/W2147632348","https://openalex.org/W2155325661","https://openalex.org/W2155358700","https://openalex.org/W2157747946","https://openalex.org/W2161201865","https://openalex.org/W2162481448","https://openalex.org/W2164281374","https://openalex.org/W2170726034","https://openalex.org/W2295428206","https://openalex.org/W3020882730","https://openalex.org/W6635637936","https://openalex.org/W6676367512","https://openalex.org/W6683767297"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W2521627374"],"abstract_inverted_index":{"Recent":[0],"interest":[1],"in":[2],"graph":[3,41],"pattern":[4],"mining":[5,42,46],"has":[6],"shifted":[7],"from":[8],"finding":[9],"all":[10],"frequent":[11,19,82,109],"subgraphs":[12,20],"to":[13,33,54,84,103],"obtaining":[14],"a":[15,56,93],"small":[16],"subset":[17],"of":[18,48,80,108,123],"that":[21,31,39,60,75,97],"are":[22],"representative,":[23],"discriminative":[24],"or":[25],"significant.":[26],"The":[27],"main":[28],"motivation":[29,52],"behind":[30],"is":[32,53,61,98],"cope":[34],"with":[35],"the":[36,40,66,77,81,105,118,124],"scalability":[37],"problem":[38],"algorithms":[43,74],"suffer":[44],"when":[45],"databases":[47],"large":[49],"graphs.":[50],"Another":[51],"obtain":[55,85],"succinct":[57],"output":[58,78,106],"set":[59],"informative":[62],"and":[63,121],"useful.":[64],"In":[65,88],"same":[67],"spirit,":[68],"researchers":[69],"also":[70],"proposed":[71,125],"sampling":[72,95,115,126],"based":[73,99],"sample":[76,104],"space":[79,107],"patterns":[83],"representative":[86],"subgraphs.":[87,110],"this":[89],"work,":[90],"we":[91],"propose":[92],"generic":[94],"framework":[96],"on":[100,113],"Metropolis-Hastings":[101],"algorithm":[102],"Our":[111],"experiments":[112],"various":[114],"strategies":[116],"show":[117],"versatility,":[119],"utility":[120],"efficiency":[122],"approach.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":11},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":13},{"year":2016,"cited_by_count":13},{"year":2015,"cited_by_count":11},{"year":2014,"cited_by_count":12},{"year":2013,"cited_by_count":14},{"year":2012,"cited_by_count":17}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}