{"id":"https://openalex.org/W3012816161","doi":"https://doi.org/10.1145/3366423.3380112","title":"Graph Representation Learning via Graphical Mutual Information Maximization","display_name":"Graph Representation Learning via Graphical Mutual Information Maximization","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012816161","doi":"https://doi.org/10.1145/3366423.3380112","mag":"3012816161"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380112","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380112","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380112","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101954954","display_name":"Zhen Peng","orcid":"https://orcid.org/0000-0001-9791-6637"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhen Peng","raw_affiliation_strings":["Xi'an Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032642601","display_name":"Wenbing Huang","orcid":"https://orcid.org/0000-0002-2566-4159"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbing Huang","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013911439","display_name":"Minnan Luo","orcid":"https://orcid.org/0000-0002-0140-7860"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minnan Luo","raw_affiliation_strings":["Xi'an Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041083459","display_name":"Qinghua Zheng","orcid":"https://orcid.org/0000-0002-8436-4754"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Zheng","raw_affiliation_strings":["Xi'an Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767600","display_name":"Yu Rong","orcid":"https://orcid.org/0000-0001-7387-302X"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Rong","raw_affiliation_strings":["Tencent AI Lab"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005345630","display_name":"Tingyang Xu","orcid":"https://orcid.org/0000-0002-8487-9045"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingyang Xu","raw_affiliation_strings":["Tencent AI Lab"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068865316","display_name":"Junzhou Huang","orcid":"https://orcid.org/0000-0002-9548-1227"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junzhou Huang","raw_affiliation_strings":["Tencent AI Lab"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101954954"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":43.8923,"has_fulltext":false,"cited_by_count":522,"citation_normalized_percentile":{"value":0.99875834,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"259","last_page":"270"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.965399980545044,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6833179593086243},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.6655306816101074},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.582490086555481},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5536558628082275},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5451390743255615},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5304538607597351},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5138295292854309},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4880322515964508},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4865964651107788},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4832223653793335},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.4274025559425354},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38889995217323303},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34812167286872864},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.25577113032341003},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19929808378219604}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6833179593086243},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.6655306816101074},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.582490086555481},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5536558628082275},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5451390743255615},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5304538607597351},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5138295292854309},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4880322515964508},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4865964651107788},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4832223653793335},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4274025559425354},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38889995217323303},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34812167286872864},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25577113032341003},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19929808378219604},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3366423.3380112","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380112","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380112","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380112","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1888005072","https://openalex.org/W1987971958","https://openalex.org/W2090891622","https://openalex.org/W2101234009","https://openalex.org/W2136144249","https://openalex.org/W2139823104","https://openalex.org/W2154851992","https://openalex.org/W2167250030","https://openalex.org/W2182190337","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2210387432","https://openalex.org/W2419501139","https://openalex.org/W2613663164","https://openalex.org/W2624431344","https://openalex.org/W2735272571","https://openalex.org/W2753798143","https://openalex.org/W2761434131","https://openalex.org/W2761896323","https://openalex.org/W2788512147","https://openalex.org/W2803832867","https://openalex.org/W2842511635","https://openalex.org/W2888657195","https://openalex.org/W2891649471","https://openalex.org/W2893944917","https://openalex.org/W2962756421","https://openalex.org/W2963416007","https://openalex.org/W2963456618","https://openalex.org/W2963521729","https://openalex.org/W2963800509","https://openalex.org/W2973488892","https://openalex.org/W2999817249","https://openalex.org/W3103296165","https://openalex.org/W3104038788","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W4232613155","https://openalex.org/W4232932184","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W4287995534","https://openalex.org/W2592385986","https://openalex.org/W2292254049","https://openalex.org/W4307326401","https://openalex.org/W2964465226","https://openalex.org/W3152463549","https://openalex.org/W3022798432","https://openalex.org/W3158586592","https://openalex.org/W2135306627","https://openalex.org/W4287763734"],"abstract_inverted_index":{"The":[0],"richness":[1],"in":[2,45,120],"the":[3,17,36,63,74,85,112,154,169,203],"content":[4],"of":[5,76,95,115,156,173,205],"various":[6],"information":[7,38,79,91,138],"networks":[8,12,15],"such":[9,141],"as":[10,142,182,184],"social":[11],"and":[13,34,68,98,133,151,171,188,199],"communication":[14],"provides":[16],"unprecedented":[18],"potential":[19],"for":[20],"learning":[21,125,162],"high-quality":[22],"expressive":[23],"representations":[24],"without":[25],"external":[26],"supervision.":[27],"This":[28],"paper":[29],"investigates":[30],"how":[31],"to":[32,61,84,111],"preserve":[33],"extract":[35],"abundant":[37],"from":[39,81,92],"graph-structured":[40],"data":[41],"into":[42],"embedding":[43],"space":[44,83],"an":[46,160],"unsupervised":[47,161,197],"manner.":[48],"To":[49],"this":[50],"end,":[51],"we":[52,158],"propose":[53],"a":[54,174],"novel":[55],"concept,":[56],"Graphical":[57],"Mutual":[58],"Information":[59],"(GMI),":[60],"measure":[62],"correlation":[64],"between":[65,168],"input":[66,116,170],"graphs":[67],"high-level":[69],"hidden":[70],"representations.":[71],"GMI":[72,103,167],"generalizes":[73],"idea":[75],"conventional":[77],"mutual":[78,90,137],"computations":[80],"vector":[82],"graph":[86,123,175],"domain":[87],"where":[88],"measuring":[89],"two":[93],"aspects":[94],"node":[96,186],"features":[97],"topological":[99],"structure":[100],"is":[101,109],"indispensable.":[102],"exhibits":[104],"several":[105],"benefits:":[106],"First,":[107],"it":[108,128],"invariant":[110],"isomorphic":[113],"transformation":[114],"graphs\u2014an":[117],"inevitable":[118],"constraint":[119],"many":[121],"existing":[122],"representation":[124],"algorithms;":[126],"Besides,":[127],"can":[129],"be":[130],"efficiently":[131],"estimated":[132],"maximized":[134],"by":[135,165],"current":[136],"estimation":[139],"methods":[140],"MINE;":[143],"Finally,":[144],"our":[145,193],"theoretical":[146],"analysis":[147],"confirms":[148],"its":[149],"correctness":[150],"rationality.":[152],"With":[153],"aid":[155],"GMI,":[157],"develop":[159],"model":[163],"trained":[164],"maximizing":[166],"output":[172],"neural":[176],"encoder.":[177],"Considerable":[178],"experiments":[179],"on":[180],"transductive":[181],"well":[183],"inductive":[185],"classification":[187],"link":[189],"prediction":[190],"demonstrate":[191],"that":[192],"method":[194],"outperforms":[195],"state-of-the-art":[196],"counterparts,":[198],"even":[200],"sometimes":[201],"exceeds":[202],"performance":[204],"supervised":[206],"ones.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":80},{"year":2024,"cited_by_count":113},{"year":2023,"cited_by_count":136},{"year":2022,"cited_by_count":106},{"year":2021,"cited_by_count":62},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
