{"id":"https://openalex.org/W4396843981","doi":"https://doi.org/10.1145/3589335.3641251","title":"Large Language Models for Graphs: Progresses and Directions","display_name":"Large Language Models for Graphs: Progresses and Directions","publication_year":2024,"publication_date":"2024-05-12","ids":{"openalex":"https://openalex.org/W4396843981","doi":"https://doi.org/10.1145/3589335.3641251"},"language":"en","primary_location":{"id":"doi:10.1145/3589335.3641251","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589335.3641251","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589335.3641251","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2024","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3589335.3641251","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091518548","display_name":"Chao Huang","orcid":"https://orcid.org/0009-0003-3740-4500"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Chao Huang","raw_affiliation_strings":["University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003117295","display_name":"Xubin Ren","orcid":"https://orcid.org/0000-0003-3332-1073"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xubin Ren","raw_affiliation_strings":["University of Kong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"University of Kong Kong, Hong Kong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101451300","display_name":"Jiabin Tang","orcid":"https://orcid.org/0009-0002-7002-3585"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jiabin Tang","raw_affiliation_strings":["University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101771060","display_name":"Dawei Yin","orcid":"https://orcid.org/0000-0002-0684-6205"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Yin","raw_affiliation_strings":["Baidu Inc., Baidu, China"],"affiliations":[{"raw_affiliation_string":"Baidu Inc., Baidu, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068157871","display_name":"Nitesh V. Chawla","orcid":"https://orcid.org/0000-0003-3932-5956"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nitesh Chawla","raw_affiliation_strings":["University of Notre Dame, South Bend, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, South Bend, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5091518548"],"corresponding_institution_ids":["https://openalex.org/I889458895"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62914525,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1284","last_page":"1287"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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":0.9940000176429749,"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":0.8070157766342163},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5539869666099548},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5034522414207458},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5015866756439209},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4876321852207184},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4768287241458893},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.46861395239830017},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4174310564994812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8070157766342163},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5539869666099548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5034522414207458},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5015866756439209},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4876321852207184},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4768287241458893},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.46861395239830017},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4174310564994812},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589335.3641251","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589335.3641251","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589335.3641251","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2024","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3589335.3641251","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589335.3641251","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589335.3641251","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2024","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4396843981.pdf"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2911286998","https://openalex.org/W3012871709","https://openalex.org/W3045200674","https://openalex.org/W3095746859","https://openalex.org/W3154503084","https://openalex.org/W4224320982","https://openalex.org/W4224325407","https://openalex.org/W4283315029","https://openalex.org/W4307003748","https://openalex.org/W4321479940","https://openalex.org/W4321479942","https://openalex.org/W4327525152","https://openalex.org/W4372279027","https://openalex.org/W4379539462","https://openalex.org/W4382317956","https://openalex.org/W4385763856","https://openalex.org/W4393065402"],"related_works":["https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W2150182025","https://openalex.org/W3092950680","https://openalex.org/W3197542405","https://openalex.org/W2056712470","https://openalex.org/W3125580266","https://openalex.org/W4288390103","https://openalex.org/W4386136067","https://openalex.org/W4286858940"],"abstract_inverted_index":{"Graph":[0],"neural":[1,101],"networks":[2],"(GNNs)":[3],"have":[4,88],"emerged":[5],"as":[6,41],"fundamental":[7],"methods":[8],"for":[9,38],"handling":[10],"structured":[11],"graph":[12,33,55,67,76,100,143],"data":[13,77],"in":[14,47],"various":[15],"domains,":[16],"including":[17],"citation":[18],"networks,":[19,102],"molecule":[20],"prediction,":[21],"and":[22,44,69,82,116],"recommender":[23],"systems.":[24],"They":[25],"enable":[26],"the":[27,48,96,112,133],"learning":[28],"of":[29,50,99,120],"informative":[30],"node":[31,45,72],"or":[32],"representations,":[34],"which":[35],"are":[36],"crucial":[37],"tasks":[39],"such":[40],"link":[42],"prediction":[43],"classification":[46],"context":[49],"graphs.":[51],"To":[52],"achieve":[53],"high-quality":[54],"representation":[56],"learning,":[57],"certain":[58],"essential":[59],"factors":[60,93],"come":[61],"into":[62],"play:":[63],"clean":[64],"labels,":[65,84],"accurate":[66],"structures,":[68],"sufficient":[70],"initial":[71],"features.":[73],"However,":[74],"real-world":[75],"often":[78],"suffer":[79],"from":[80],"noise":[81],"sparse":[83],"while":[85],"different":[86],"datasets":[87],"unique":[89],"feature":[90],"constructions.":[91],"These":[92],"significantly":[94],"impact":[95],"generalization":[97,118],"capabilities":[98],"particularly":[103],"when":[104],"faced":[105],"with":[106,142],"unseen":[107],"tasks.":[108],"Recently,":[109],"due":[110],"to":[111,131],"efficent":[113],"text":[114],"processing":[115],"task":[117],"capability":[119],"large":[121,139],"language":[122,140],"models":[123,141],"(LLMs),":[124],"there":[125],"has":[126],"been":[127],"a":[128],"promising":[129],"approach":[130],"address":[132],"challenges":[134],"mentioned":[135],"above":[136],"by":[137],"combining":[138],"data.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}