{"id":"https://openalex.org/W4416016539","doi":"https://doi.org/10.1145/3746252.3761102","title":"D-HAT: Dynamic Hypergraph Representation Learning with Attention-Based Multi-Level Hypergraph Sampling","display_name":"D-HAT: Dynamic Hypergraph Representation Learning with Attention-Based Multi-Level Hypergraph Sampling","publication_year":2025,"publication_date":"2025-11-08","ids":{"openalex":"https://openalex.org/W4416016539","doi":"https://doi.org/10.1145/3746252.3761102"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761102","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761102","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 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3746252.3761102","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073927965","display_name":"Ah-hyun Lee","orcid":"https://orcid.org/0009-0008-5399-4076"},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ah-hyun Lee","raw_affiliation_strings":["Sogang University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0008-5399-4076","affiliations":[{"raw_affiliation_string":"Sogang University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I148751991"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023057376","display_name":"Gordon Euhyun Moon","orcid":"https://orcid.org/0000-0003-4992-6181"},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Gordon Euhyun Moon","raw_affiliation_strings":["Sogang University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-4992-6181","affiliations":[{"raw_affiliation_string":"Sogang University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I148751991"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15558857,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1428","last_page":"1437"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9570000171661377,"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":0.9570000171661377,"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":0.006300000008195639,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.004800000227987766,"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":0.9599000215530396},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4959999918937683},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.47699999809265137},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46399998664855957},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44780001044273376},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.41749998927116394},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4083000123500824}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.9599000215530396},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5712000131607056},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5297999978065491},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4959999918937683},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44780001044273376},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.41749998927116394},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36809998750686646},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3409000039100647},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3314000070095062},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32109999656677246},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29910001158714294},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761102","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761102","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 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3746252.3761102","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761102","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 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2108614537","https://openalex.org/W2162630660","https://openalex.org/W2892880750","https://openalex.org/W2963446712","https://openalex.org/W3035520037","https://openalex.org/W3085990079","https://openalex.org/W3106229813","https://openalex.org/W3128617813","https://openalex.org/W3130518848","https://openalex.org/W4224314061","https://openalex.org/W4282926996","https://openalex.org/W4285606137","https://openalex.org/W4385286258","https://openalex.org/W4385636751","https://openalex.org/W4390873987","https://openalex.org/W4401863422"],"related_works":[],"abstract_inverted_index":{"Hypergraph":[0],"Neural":[1],"Networks":[2],"(HNNs)":[3],"leverage":[4],"higher-order":[5],"interactions":[6],"in":[7,29,74],"graph-structured":[8],"data":[9],"to":[10,22,35,71,147,174],"enable":[11],"effective":[12],"representation":[13,156],"learning":[14,90],"across":[15],"a":[16,37,88,98,125],"wide":[17],"range":[18],"of":[19,25,67,78,108,119,139,166],"applications.":[20],"Due":[21],"the":[23,72,106,116,137,145,164],"issue":[24],"incorporating":[26,129],"irrelevant":[27],"relations":[28],"full":[30],"hypergraphs,":[31],"it":[32],"is":[33,144],"crucial":[34],"adopt":[36],"hypergraph":[38,51,89,102,151,155,176],"sampling":[39,52,75,103,152,177],"method":[40],"that":[41,54,92],"efficiently":[42],"captures":[43],"substructures":[44],"while":[45],"preserving":[46],"representational":[47,117],"quality.":[48],"However,":[49],"existing":[50,175],"methods":[53,153],"target":[55],"only":[56],"nodes":[57],"or":[58],"hyperedges":[59],"suffer":[60],"from":[61],"subgraph":[62],"disconnection":[63],"issues":[64],"and":[65,76,114,132],"neglect":[66],"node":[68],"importance":[69],"due":[70],"randomness":[73],"use":[77],"static":[79],"computational":[80],"sub-hypergraphs.":[81],"In":[82],"this":[83,142],"paper,":[84],"we":[85,123],"propose":[86],"D-HAT,":[87,167],"framework":[91],"dynamically":[93],"constructs":[94],"representative":[95],"sub-hypergraphs":[96,120],"through":[97],"novel":[99],"attention-based":[100,127],"multi-level":[101],"strategy":[104],"during":[105,121],"training":[107],"HNNs.":[109],"To":[110,136],"prioritize":[111],"informative":[112],"neighbors":[113],"enhance":[115],"quality":[118],"training,":[122],"develop":[124],"new":[126],"HNNs":[128],"attention-guided":[130],"aggregation":[131],"dense":[133],"skip":[134],"connections.":[135],"best":[138],"our":[140],"knowledge,":[141],"paper":[143],"first":[146],"quantitatively":[148],"compare":[149],"various":[150],"for":[154],"learning.":[157],"Experiments":[158],"on":[159],"real-world":[160],"graph":[161],"datasets":[162],"demonstrate":[163],"effectiveness":[165],"which":[168],"consistently":[169],"achieves":[170],"higher":[171],"accuracy":[172],"compared":[173],"methods.":[178]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-08T00:00:00"}