{"id":"https://openalex.org/W4387846328","doi":"https://doi.org/10.1145/3583780.3614825","title":"Contrastive Representation Learning Based on Multiple Node-centered Subgraphs","display_name":"Contrastive Representation Learning Based on Multiple Node-centered Subgraphs","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846328","doi":"https://doi.org/10.1145/3583780.3614825"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3614825","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3614825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","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/A5115603891","display_name":"Dong Li","orcid":"https://orcid.org/0000-0003-0081-9318"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dong Li","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021683707","display_name":"W. Wang","orcid":"https://orcid.org/0000-0002-8685-9056"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjun Wang","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101594410","display_name":"Minglai Shao","orcid":"https://orcid.org/0000-0003-1830-9797"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minglai Shao","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100767050","display_name":"Chen Zhao","orcid":"https://orcid.org/0000-0002-6400-0048"},"institutions":[{"id":"https://openalex.org/I157394403","display_name":"Baylor University","ror":"https://ror.org/005781934","country_code":"US","type":"education","lineage":["https://openalex.org/I157394403"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Zhao","raw_affiliation_strings":["Baylor University, Waco, TX, USA"],"affiliations":[{"raw_affiliation_string":"Baylor University, Waco, TX, USA","institution_ids":["https://openalex.org/I157394403"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5115603891"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.5281,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72504199,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1338","last_page":"1347"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9901999831199646,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9793999791145325,"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":0.6973690390586853},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.6524150967597961},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5570491552352905},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.511938214302063},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4991292953491211},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.48235195875167847},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39461782574653625}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6973690390586853},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.6524150967597961},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5570491552352905},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.511938214302063},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4991292953491211},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.48235195875167847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39461782574653625},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3614825","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3614825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.4300000071525574}],"awards":[{"id":"https://openalex.org/G1357398533","display_name":null,"funder_award_id":"2022LHMS06008","funder_id":"https://openalex.org/F4320322868","funder_display_name":"Natural Science Foundation of Inner Mongolia"},{"id":"https://openalex.org/G1562177963","display_name":null,"funder_award_id":"62272338","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322868","display_name":"Natural Science Foundation of Inner Mongolia","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2069153192","https://openalex.org/W2135198476","https://openalex.org/W2154851992","https://openalex.org/W2168627253","https://openalex.org/W2913668833","https://openalex.org/W2962756421","https://openalex.org/W2972209102","https://openalex.org/W2999905431","https://openalex.org/W3003257608","https://openalex.org/W3012816161","https://openalex.org/W3023371261","https://openalex.org/W3033091443","https://openalex.org/W3036446966","https://openalex.org/W3099152386","https://openalex.org/W3104097132","https://openalex.org/W3108655343","https://openalex.org/W3122451732","https://openalex.org/W3126928293","https://openalex.org/W3165956705","https://openalex.org/W3204453541","https://openalex.org/W4224314952","https://openalex.org/W6755573351"],"related_works":["https://openalex.org/W2364252372","https://openalex.org/W4234066492","https://openalex.org/W1998063895","https://openalex.org/W1967044713","https://openalex.org/W2133470120","https://openalex.org/W1994286895","https://openalex.org/W2747625183","https://openalex.org/W4285218279","https://openalex.org/W4386136067","https://openalex.org/W4286858940"],"abstract_inverted_index":{"As":[0],"the":[1,12,30,54,91,95,102],"basic":[2],"element":[3],"of":[4,15,56,86,90,101],"graph-structured":[5],"data,":[6],"node":[7,23,72,104],"has":[8,25,40,123],"been":[9],"recognized":[10],"as":[11],"main":[13],"object":[14],"study":[16,50],"in":[17,36,76],"graph":[18,32,57],"representation":[19,67,73],"learning.":[20],"A":[21],"single":[22],"intuitively":[24],"multiple":[26,41,63],"node-centered":[27,64,87],"subgraphs":[28,65,89,100],"from":[29],"whole":[31],"(e.g.,":[33],"one":[34],"person":[35],"a":[37,62,77,84],"social":[38,42],"network":[39],"circles":[43],"based":[44],"on":[45,74,111],"his":[46],"different":[47,99,116],"relationships).":[48],"We":[49],"this":[51],"intuition":[52],"under":[53],"framework":[55],"contrastive":[58,66,108],"learning,":[59],"and":[60,115],"propose":[61],"learning":[68],"method":[69],"to":[70],"learn":[71],"graphs":[75],"self-supervised":[78],"way.":[79],"Specifically,":[80],"we":[81],"carefully":[82],"design":[83],"series":[85],"regional":[88],"central":[92],"node.":[93],"Then,":[94],"mutual":[96],"information":[97],"between":[98],"same":[103],"is":[105],"maximized":[106],"by":[107],"loss.":[109],"Experiments":[110],"various":[112],"real-world":[113],"datasets":[114],"downstream":[117],"tasks":[118],"demonstrate":[119],"that":[120],"our":[121],"model":[122],"achieved":[124],"state-of-the-art":[125],"results.":[126]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
