{"id":"https://openalex.org/W4416034757","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.360","title":"HEAL: Hybrid Enhancement with LLM-based Agents for Text-attributed Hypergraph Self-supervised Representation Learning","display_name":"HEAL: Hybrid Enhancement with LLM-based Agents for Text-attributed Hypergraph Self-supervised Representation Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034757","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.360"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.360","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.360","pdf_url":"https://aclanthology.org/2025.findings-emnlp.360.pdf","source":null,"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: EMNLP 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-emnlp.360.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017008500","display_name":"Ruochang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruochang Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100426938","display_name":"Xiao Luo","orcid":"https://orcid.org/0000-0002-7987-3714"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Luo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049472306","display_name":"Zhiping Xiao","orcid":"https://orcid.org/0000-0002-8583-4789"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiping Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018666299","display_name":"Wei Ju","orcid":"https://orcid.org/0000-0001-9657-951X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Ju","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100642537","display_name":"Ming Zhang","orcid":"https://orcid.org/0000-0002-9809-3430"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15746381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6815","last_page":"6829"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.45989999175071716,"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":0.45989999175071716,"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":0.1800999939441681,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.09929999709129333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.5911999940872192},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4921000003814697},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3206999897956848},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3133000135421753},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.27869999408721924},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.2732999920845032}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.5911999940872192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5428000092506409},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4921000003814697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4918000102043152},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40630000829696655},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3353999853134155},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30730000138282776},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2712000012397766},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.360","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.360","pdf_url":"https://aclanthology.org/2025.findings-emnlp.360.pdf","source":null,"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: EMNLP 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.360","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.360","pdf_url":"https://aclanthology.org/2025.findings-emnlp.360.pdf","source":null,"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: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034757.pdf","grobid_xml":"https://content.openalex.org/works/W4416034757.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"studies":[2],"the":[3,34,101,113,121,129,143,167],"problem":[4],"of":[5,17,74,145,169],"textattributed":[6],"hypergraph":[7,56],"self-supervised":[8,57,156],"representation":[9,35],"learning,":[10],"which":[11,31],"aims":[12],"to":[13,54,78,99,138],"generate":[14,79,108],"discriminative":[15,160],"representations":[16],"hypergraphs":[18,26,150],"without":[19],"any":[20],"annotations":[21],"for":[22,96,105,115,142,159],"downstream":[23],"tasks.However,":[24],"real-world":[25],"could":[27,32],"contain":[28],"incomplete":[29],"signals,":[30],"deteriorate":[33],"learning":[36,58,157],"procedure,":[37],"especially":[38],"under":[39],"label":[40],"scarcity.Towards":[41],"this":[42],"end,":[43],"we":[44,91,111,132],"introduce":[45],"a":[46,61,134,155],"new":[47,140],"perspective":[48],"that":[49],"leverages":[50],"large":[51],"language":[52],"models":[53],"enhance":[55],"and":[59,82,119],"propose":[60],"novel":[62],"data-centric":[63],"approach":[64],"named":[65],"Hybrid":[66],"Hypergraph":[67],"Enhancement":[68],"with":[69,87,174],"LLM-based":[70],"Agents":[71],"(HEAL).The":[72],"core":[73],"our":[75,170],"HEAL":[76,171],"is":[77],"informative":[80,123],"nodes":[81],"hyperedges":[83,141],"through":[84],"multi-round":[85],"interaction":[86],"LLMbased":[88],"agents.In":[89],"particular,":[90],"first":[92],"retrieve":[93],"similar":[94],"samples":[95],"each":[97,116],"node":[98,102],"facilitate":[100],"expansion":[103],"agent":[104,137],"different":[106],"views.To":[107],"challenging":[109],"samples,":[110],"measure":[112],"gradients":[114],"augmented":[117],"view":[118],"select":[120],"most":[122],"one":[124],"using":[125],"an":[126],"evaluation":[127],"agent.From":[128],"structural":[130,147],"view,":[131],"adopt":[133],"topology":[135],"refinement":[136],"incorporate":[139],"recovery":[144],"missing":[146],"signals.The":[148],"enhanced":[149],"would":[151],"be":[152],"incorporated":[153],"into":[154],"framework":[158],"representations.Extensive":[161],"experiments":[162],"on":[163],"several":[164],"datasets":[165],"validate":[166],"effectiveness":[168],"in":[172],"comparison":[173],"extensive":[175],"baselines.":[176]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-08T00:00:00"}
