{"id":"https://openalex.org/W7126466349","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.132","title":"Language is All a Graph Needs","display_name":"Language is All a Graph Needs","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W7126466349","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.132"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2024.findings-eacl.132","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.132","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.132","pdf_url":"https://aclanthology.org/2024.findings-eacl.132.pdf","source":{"id":"https://openalex.org/S7407087051","display_name":"Findings of the Association for Computational Linguistics: EACL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EACL 2024","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2024.findings-eacl.132.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067846528","display_name":"Ruosong Ye","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruosong Ye","raw_affiliation_strings":["Department of Computer Science , Rutgers University , New Brunswick , US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science , Rutgers University , New Brunswick , US","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124670816","display_name":"Caiqi Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Caiqi Zhang","raw_affiliation_strings":["Language Technology Lab , University of Cambridge , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Language Technology Lab , University of Cambridge , UK","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042205793","display_name":"Runhui Wang","orcid":"https://orcid.org/0000-0003-0823-5982","observed_orcids":["https://orcid.org/0000-0003-0823-5982"]},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Runhui Wang","raw_affiliation_strings":["Department of Computer Science , Rutgers University , New Brunswick , US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science , Rutgers University , New Brunswick , US","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124483383","display_name":"Shuyuan Xu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuyuan Xu","raw_affiliation_strings":["Department of Computer Science , Rutgers University , New Brunswick , US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science , Rutgers University , New Brunswick , US","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124672951","display_name":"Yongfeng Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongfeng Zhang","raw_affiliation_strings":["Department of Computer Science , Rutgers University , New Brunswick , US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science , Rutgers University , New Brunswick , US","institution_ids":["https://openalex.org/I102322142"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":26.721,"has_fulltext":true,"cited_by_count":189,"citation_normalized_percentile":{"value":0.99687272,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1955","last_page":"1973"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9990000128746033,"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":0.9990000128746033,"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":0.0003000000142492354,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/large-language-models","display_name":"large language models","score":0.7699999809265137},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.734000027179718},{"id":"https://openalex.org/keywords/instruction-tuning","display_name":"instruction tuning","score":0.7710000276565552},{"id":"https://openalex.org/keywords/graph-foundation-models","display_name":"graph foundation models","score":0.6399999856948853},{"id":"https://openalex.org/keywords/natural-language-prompts","display_name":"natural language prompts","score":0.5580000281333923},{"id":"https://openalex.org/keywords/graph-learning","display_name":"graph learning","score":0.5789999961853027}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7526000142097473},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7026000022888184},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.6510000228881836},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5515000224113464},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47040000557899475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4684999883174896},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C83479923","wikidata":"https://www.wikidata.org/wiki/Q2063748","display_name":"Universal Networking Language","level":4,"score":0.36579999327659607},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.3537999987602234},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.35089999437332153},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.3456999957561493},{"id":"https://openalex.org/C67463725","wikidata":"https://www.wikidata.org/wiki/Q17081452","display_name":"Natural language programming","level":5,"score":0.33719998598098755},{"id":"https://openalex.org/C129792486","wikidata":"https://www.wikidata.org/wiki/Q1050419","display_name":"Language identification","level":3,"score":0.3246000111103058},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30410000681877136},{"id":"https://openalex.org/C14919245","wikidata":"https://www.wikidata.org/wiki/Q1976109","display_name":"Language technology","level":4,"score":0.28380000591278076},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.27900001406669617},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2780878386","wikidata":"https://www.wikidata.org/wiki/Q1659648","display_name":"Visual language","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2024.findings-eacl.132","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.132","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.132","pdf_url":"https://aclanthology.org/2024.findings-eacl.132.pdf","source":{"id":"https://openalex.org/S7407087051","display_name":"Findings of the Association for Computational Linguistics: EACL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EACL 2024","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2308.07134","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.07134","pdf_url":"https://arxiv.org/pdf/2308.07134","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2308.07134","doi":"https://doi.org/10.48550/arxiv.2308.07134","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2308.07134","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/2024.findings-eacl.132","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.132","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.132","pdf_url":"https://aclanthology.org/2024.findings-eacl.132.pdf","source":{"id":"https://openalex.org/S7407087051","display_name":"Findings of the Association for Computational Linguistics: EACL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EACL 2024","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7126466349.pdf","grobid_xml":"https://content.openalex.org/works/W7126466349.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W2519887557","https://openalex.org/W2766453196","https://openalex.org/W2894175828","https://openalex.org/W2896457183","https://openalex.org/W2908510526","https://openalex.org/W2950635152","https://openalex.org/W2965373594","https://openalex.org/W2995509042","https://openalex.org/W2996268457","https://openalex.org/W3013034147","https://openalex.org/W3035649237","https://openalex.org/W3037242362","https://openalex.org/W3094502228","https://openalex.org/W3098267758","https://openalex.org/W3113177135","https://openalex.org/W3122063025","https://openalex.org/W3136305959","https://openalex.org/W3152956381","https://openalex.org/W3168867926","https://openalex.org/W3174770825","https://openalex.org/W3175287561","https://openalex.org/W3187966659","https://openalex.org/W3189324775","https://openalex.org/W3198002980","https://openalex.org/W3210237961","https://openalex.org/W3216565018","https://openalex.org/W4221150379","https://openalex.org/W4225104649","https://openalex.org/W4294786649","https://openalex.org/W4307079201","https://openalex.org/W4313483544","https://openalex.org/W4322718191","https://openalex.org/W4360836968","https://openalex.org/W4366999191","https://openalex.org/W4377090137","https://openalex.org/W4378465454","https://openalex.org/W4379087246","https://openalex.org/W4382202870","https://openalex.org/W4382319000","https://openalex.org/W4383180590","https://openalex.org/W4384616153","https://openalex.org/W4385004765","https://openalex.org/W4385680954","https://openalex.org/W4386804245","https://openalex.org/W4389520779","https://openalex.org/W4390692489","https://openalex.org/W4391506272","https://openalex.org/W4391558151","https://openalex.org/W4393277515","https://openalex.org/W4400528556","https://openalex.org/W4402670285","https://openalex.org/W6906815983","https://openalex.org/W7133206817","https://openalex.org/W7133244260"],"related_works":["https://openalex.org/W4402683734","https://openalex.org/W4416252029","https://openalex.org/W4413640150","https://openalex.org/W4404688875","https://openalex.org/W4412887821","https://openalex.org/W4403486658","https://openalex.org/W4405254610","https://openalex.org/W4392224151","https://openalex.org/W7160933274","https://openalex.org/W4392617522"],"abstract_inverted_index":{"The":[0],"emergence":[1],"of":[2,26,57,85,130],"large-scale":[3],"pre-trained":[4],"language":[5,31,76,120,124],"models":[6],"has":[7],"revolutionized":[8],"various":[9],"AI":[10],"research":[11],"domains.Transformers-based":[12],"Large":[13],"Language":[14,111],"Models":[15],"(LLMs)":[16],"have":[17],"gradually":[18],"replaced":[19],"CNNs":[20],"and":[21,29,48,133,156,162],"RNNs":[22],"to":[23,90,125,139],"unify":[24],"fields":[25],"computer":[27],"vision":[28],"natural":[30,53,119,123],"processing.Compared":[32],"with":[33,113],"independent":[34],"data":[35],"samples":[36],"such":[37],"as":[38,98,168],"images,":[39],"videos":[40],"or":[41],"texts,":[42],"graphs":[43],"usually":[44],"contain":[45],"rich":[46],"structural":[47],"relational":[49],"information.Meanwhile,":[50],"language,":[51,54],"especially":[52],"being":[55],"one":[56],"the":[58,74,82,99,131],"most":[59],"expressive":[60],"mediums,":[61],"excels":[62],"in":[63],"describing":[64],"complex":[65],"structures.However,":[66],"existing":[67],"work":[68],"on":[69,118,153,165],"incorporating":[70],"graph":[71,132,141,173],"problems":[72],"into":[73],"generative":[75,166],"modeling":[77],"framework":[78],"remains":[79],"very":[80],"limited.Considering":[81],"rising":[83],"prominence":[84],"LLMs,":[86],"it":[87],"becomes":[88],"essential":[89],"explore":[91],"whether":[92],"LLMs":[93,167],"can":[94],"also":[95],"replace":[96],"GNNs":[97],"foundation":[100,170],"model":[101,171],"for":[102,172],"graphs.In":[103],"this":[104],"paper,":[105],"we":[106],"propose":[107],"Instruct-GLM":[108],"(Instruction-finetuned":[109],"Graph":[110,146],"Model)":[112],"highly":[114],"scalable":[115],"prompts":[116],"based":[117],"instructions.We":[121],"use":[122],"describe":[126],"multi-scale":[127],"geometric":[128],"structure":[129],"then":[134],"instruction":[135],"finetune":[136],"an":[137],"LLM":[138],"perform":[140],"tasks,":[142],"which":[143],"enables":[144],"Generative":[145],"Learning.Our":[147],"method":[148],"surpasses":[149],"all":[150],"GNN":[151],"baselines":[152],"ogbnarxiv,":[154],"Cora":[155],"PubMed":[157],"datasets,":[158],"underscoring":[159],"its":[160],"effectiveness":[161],"sheds":[163],"light":[164],"new":[169],"machine":[174],"learning.Our":[175],"code":[176],"is":[177],"available":[178],"at":[179],"https://github.com/agiresearch/InstructGLM.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":46},{"year":2025,"cited_by_count":67},{"year":2024,"cited_by_count":63},{"year":2023,"cited_by_count":13}],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-02-02T00:00:00"}