{"id":"https://openalex.org/W3007626807","doi":"https://doi.org/10.1109/bigdata47090.2019.9005553","title":"Generalizing Design of Support Measures for Counting Frequent Patterns in Graphs","display_name":"Generalizing Design of Support Measures for Counting Frequent Patterns in Graphs","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007626807","doi":"https://doi.org/10.1109/bigdata47090.2019.9005553","mag":"3007626807"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10845168/pdf/nihms-1959082.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101059075","display_name":"Jinghan Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinghan Meng","raw_affiliation_strings":["Dept. of Computer Science, University of South Florida, Tampa, Florida, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, University of South Florida, Tampa, Florida, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010665954","display_name":"Napath Pitaksirianan","orcid":"https://orcid.org/0000-0001-9837-2950"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Napath Pitaksirianan","raw_affiliation_strings":["Dept. of Computer Science, University of South Florida, Tampa, Florida, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, University of South Florida, Tampa, Florida, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032673243","display_name":"Yi-Cheng Tu","orcid":"https://orcid.org/0000-0002-4062-2694"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yicheng Tu","raw_affiliation_strings":["Dept. of Computer Science, University of South Florida, Tampa, Florida, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, University of South Florida, Tampa, Florida, USA","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7053,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81036149,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"533","last_page":"542"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9965000152587891,"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":0.9965000152587891,"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/T11106","display_name":"Data Management and Algorithms","score":0.9962999820709229,"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"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9958000183105469,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.8036417961120605},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.64464271068573},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6154415607452393},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5460505485534668},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5386452674865723},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4947147071361542},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4731498956680298},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4594671428203583},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2761247754096985},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21675586700439453},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.12457334995269775}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.8036417961120605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.64464271068573},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6154415607452393},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5460505485534668},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5386452674865723},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4947147071361542},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4731498956680298},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4594671428203583},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2761247754096985},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21675586700439453},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.12457334995269775},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9005553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:10845168","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10845168","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10845168/pdf/nihms-1959082.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IEEE Int Conf Big Data","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:10845168","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10845168","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10845168/pdf/nihms-1959082.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IEEE Int Conf Big Data","raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3007626807.pdf","grobid_xml":"https://content.openalex.org/works/W3007626807.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W174207204","https://openalex.org/W1510837191","https://openalex.org/W1881940640","https://openalex.org/W1966923282","https://openalex.org/W1985644065","https://openalex.org/W2003813631","https://openalex.org/W2019827052","https://openalex.org/W2024662824","https://openalex.org/W2039393227","https://openalex.org/W2076680677","https://openalex.org/W2078789330","https://openalex.org/W2092676508","https://openalex.org/W2101654328","https://openalex.org/W2106202847","https://openalex.org/W2115954767","https://openalex.org/W2122255072","https://openalex.org/W2123871098","https://openalex.org/W2126519339","https://openalex.org/W2151502664","https://openalex.org/W2153301229","https://openalex.org/W2170726034","https://openalex.org/W2415186390","https://openalex.org/W2613522499","https://openalex.org/W3007626807","https://openalex.org/W6607077362","https://openalex.org/W6685146747","https://openalex.org/W6774387830"],"related_works":["https://openalex.org/W4376608589","https://openalex.org/W1537073411","https://openalex.org/W3138003926","https://openalex.org/W1630514295","https://openalex.org/W4300037846","https://openalex.org/W4288275998","https://openalex.org/W2963081352","https://openalex.org/W4376608938","https://openalex.org/W2472555608","https://openalex.org/W2477549100"],"abstract_inverted_index":{"Frequent":[0],"subgraph":[1],"mining":[2],"(FSM)":[3],"from":[4,157],"graphs":[5],"is":[6,19],"an":[7],"active":[8],"subject":[9],"in":[10,17,37,42,106,123,169],"computer":[11],"science":[12],"research.":[13],"One":[14],"major":[15],"challenge":[16],"FSM":[18],"the":[20,55,62,77,81,90,124,130,158,163],"development":[21,91],"of":[22,59,79,92],"support":[23,60,94,104,117,173],"measures,":[24,95],"which":[25,53,109],"are":[26,120],"basically":[27],"functions":[28],"that":[29,119,148],"map":[30],"a":[31,38,46,149],"pattern":[32,51],"to":[33,113,133],"its":[34],"frequency":[35],"count":[36],"database.":[39],"Current":[40],"state-of-the-art":[41],"this":[43],"topic":[44],"features":[45],"hypergraph-based":[47],"framework":[48],"for":[49,101,140],"modeling":[50],"occurrences":[52],"unifies":[54],"two":[56,86],"main":[57],"flavors":[58],"measures:":[61],"overlap-graph":[63],"based":[64,73],"maximum":[65,150],"independent":[66,151],"set":[67,153],"measure":[68,138,155],"(MIS)":[69],"and":[70,88,115,136,167,172],"minimum":[71],"image/instance":[72],"(MNI)":[74],"measures.":[75,144],"For":[76],"purpose":[78],"exploring":[80],"middle":[82],"ground":[83],"between":[84,165],"these":[85],"groups":[87],"guiding":[89],"new":[93,103],"we":[96,146],"present":[97],"general":[98],"sufficient":[99,131,159],"conditions":[100,132,160],"designing":[102,141],"measures":[105,118],"hypergraph":[107],"framework,":[108],"can":[110,161],"be":[111],"applied":[112],"MNI":[114,135],"other":[116],"not":[121],"included":[122],"overlap":[125],"graph":[126],"framework.":[127],"We":[128],"utilize":[129],"generalize":[134],"minimum-instance":[137],"(MI)":[139],"user-defined":[142],"linear-time":[143],"Furthermore,":[145],"show":[147],"subedge":[152],"(MISS)":[154],"developed":[156],"fill":[162],"gap":[164],"MIS":[166],"MI":[168],"computation":[170],"complexity":[171],"count.":[174]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2020-03-06T00:00:00"}
