{"id":"https://openalex.org/W4205522633","doi":"https://doi.org/10.1109/bigdata52589.2021.9671652","title":"Graph Compression Networks","display_name":"Graph Compression Networks","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205522633","doi":"https://doi.org/10.1109/bigdata52589.2021.9671652"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671652","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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/A5082065117","display_name":"Ting Guo","orcid":"https://orcid.org/0000-0001-5130-3237"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Ting Guo","raw_affiliation_strings":["Data Science Institute, University of Technology, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Data Science Institute, University of Technology, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084641325","display_name":"Xingquan Zhu","orcid":"https://orcid.org/0000-0003-4129-9611"},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xingquan Zhu","raw_affiliation_strings":["Dept. of Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, USA","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100714578","display_name":"Yang Wang","orcid":"https://orcid.org/0000-0002-6815-0879"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yang Wang","raw_affiliation_strings":["Data Science Institute, University of Technology, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Data Science Institute, University of Technology, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100400043","display_name":"Fang Chen","orcid":"https://orcid.org/0000-0003-4971-8729"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Fang Chen","raw_affiliation_strings":["Data Science Institute, University of Technology, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Data Science Institute, University of Technology, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5082065117"],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.18283791,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1030","last_page":"1036"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11478","display_name":"Caching and Content Delivery","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7775061130523682},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5363932847976685},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.47462308406829834},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.44809088110923767},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.427359402179718},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.42705482244491577},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41501113772392273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29806119203567505}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7775061130523682},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5363932847976685},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.47462308406829834},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.44809088110923767},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.427359402179718},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.42705482244491577},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41501113772392273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29806119203567505},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671652","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1662382123","https://openalex.org/W1959608418","https://openalex.org/W2017403239","https://openalex.org/W2047940964","https://openalex.org/W2127048411","https://openalex.org/W2135957668","https://openalex.org/W2142498761","https://openalex.org/W2154851992","https://openalex.org/W2168627253","https://openalex.org/W2380769351","https://openalex.org/W2393319904","https://openalex.org/W2405933695","https://openalex.org/W2465015709","https://openalex.org/W2767404761","https://openalex.org/W2770480868","https://openalex.org/W2808409763","https://openalex.org/W2809343047","https://openalex.org/W2899831870","https://openalex.org/W2951659295","https://openalex.org/W2963224980","https://openalex.org/W2963555845","https://openalex.org/W2964015378","https://openalex.org/W2964321699","https://openalex.org/W2997785591","https://openalex.org/W3007364240","https://openalex.org/W3102641634","https://openalex.org/W3104097132","https://openalex.org/W3108572544","https://openalex.org/W3122998099","https://openalex.org/W4294558607","https://openalex.org/W4322614756","https://openalex.org/W6637178625","https://openalex.org/W6640963894","https://openalex.org/W6681029592","https://openalex.org/W6719270105","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6730084236","https://openalex.org/W6738964360","https://openalex.org/W6753331806","https://openalex.org/W6756506996","https://openalex.org/W6772032083","https://openalex.org/W6774403930"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W4285218279"],"abstract_inverted_index":{"Graphs/Networks":[0],"are":[1,117],"common":[2],"in":[3,183],"real-world":[4],"applications":[5],"where":[6],"data":[7],"have":[8,57],"rich":[9],"content":[10,74],"and":[11,29,72,90,214,228,237,239,259],"complex":[12],"relationships.":[13],"The":[14],"increasing":[15],"popularity":[16],"also":[17,142,215,246],"motivates":[18],"many":[19],"network":[20,37,63,70,84,88,102,198,209],"learning":[21,45,177],"algorithms,":[22],"such":[23,108],"as":[24,119,242],"community":[25],"detection,":[26],"clustering,":[27],"classification,":[28],"embedding":[30,91],"learning,":[31,140],"etc..":[32],"In":[33,76,136],"reality,":[34],"the":[35,48,58,93,101,133,154,184,195,219],"large":[36],"volumes":[38],"often":[39],"hider":[40],"a":[41,51,62,81,120,124,144,208],"direct":[42],"use":[43],"of":[44,189],"algorithms":[46],"to":[47,56,60,64,86,99,104,131,138,152,176,200,210,218],"graphs.":[49],"As":[50],"result,":[52],"it":[53,193,217],"is":[54,98,191],"desirable":[55],"flexibility":[59],"condense":[61],"an":[65,211],"arbitrary":[66,212],"size,":[67,213],"with":[68,123,223],"well-preserved":[69],"topology":[71,103],"node":[73,106,115,221,260],"information.":[75],"this":[77],"paper,":[78],"we":[79,141],"propose":[80],"graph":[82,160,196,257],"compression":[83,89,139],"(GEN)":[85],"achieve":[87],"at":[92],"same":[94],"time.":[95],"Our":[96],"theme":[97],"leverage":[100],"find":[105,235],"mappings,":[107],"that":[109,192,231,248],"densely":[110],"connected":[111],"nodes,":[112],"including":[113,256],"their":[114],"content,":[116],"compressed":[118,134,174],"new":[121,243],"node,":[122],"latent":[125],"vector":[126],"(i.e.":[127],"embedding)":[128],"being":[129],"learned":[130],"represent":[132],"node.":[135],"addition":[137],"develop":[143],"novel":[145],"encoding-decoding":[146],"framework,":[147],"using":[148],"feature":[149,178],"diffusion":[150],"process,":[151],"\"decompress\"":[153],"condensed":[155],"network.":[156,185],"Different":[157],"from":[158],"traditional":[159],"convolution":[161],"which":[162],"uses":[163],"direct-neighbor":[164],"message":[165,171],"passing,":[166],"our":[167],"decompression":[168],"advocates":[169],"high-order":[170],"passing":[172],"within":[173],"nodes":[175,182],"representation":[179],"for":[180,253],"all":[181],"A":[186],"unique":[187],"strength":[188],"GEN":[190,232,249],"leverages":[194],"neural":[197],"principle":[199],"learn":[201],"mapping":[202],"automatically,":[203],"so":[204],"one":[205],"can":[206,233],"compress":[207,240],"decompress":[216],"original":[220],"space":[222],"minimum":[224],"information":[225],"loss.":[226],"Experiments":[227],"comparisons":[229],"confirm":[230],"automatically":[234],"clusters":[236],"communities,":[238],"them":[241],"nodes.":[244],"Results":[245],"show":[247],"achieves":[250],"improved":[251],"performance":[252],"numerous":[254],"tasks,":[255],"classification":[258],"clustering.":[261]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
