{"id":"https://openalex.org/W7138041567","doi":"https://doi.org/10.1609/aaai.v40i24.39068","title":"Spiking Heterogeneous Graph Attention Networks","display_name":"Spiking Heterogeneous Graph Attention Networks","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138041567","doi":"https://doi.org/10.1609/aaai.v40i24.39068"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i24.39068","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i24.39068","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39068/43030","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39068/43030","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129747184","display_name":"Buqing Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Buqing Cao","raw_affiliation_strings":["Hunan University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University of Technology","institution_ids":["https://openalex.org/I49934816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101710221","display_name":"Qian Peng","orcid":"https://orcid.org/0000-0003-2893-7041"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Peng","raw_affiliation_strings":["Central South University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Central South University","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129645970","display_name":"Xiang Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I121296143","display_name":"Hunan University of Science and Technology","ror":"https://ror.org/02m9vrb24","country_code":"CN","type":"education","lineage":["https://openalex.org/I121296143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Xie","raw_affiliation_strings":["Hunan University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University of Science and Technology","institution_ids":["https://openalex.org/I121296143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129710830","display_name":"Liang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang Chen","raw_affiliation_strings":["Sun Yat-Sen University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129731249","display_name":"Min Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I79516672","display_name":"University of Louisiana at Lafayette","ror":"https://ror.org/01x8rc503","country_code":"US","type":"education","lineage":["https://openalex.org/I2799628689","https://openalex.org/I79516672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Min Shi","raw_affiliation_strings":["University of Louisiana at Lafeyette"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Louisiana at Lafeyette","institution_ids":["https://openalex.org/I79516672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129740048","display_name":"Jianxun Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I121296143","display_name":"Hunan University of Science and Technology","ror":"https://ror.org/02m9vrb24","country_code":"CN","type":"education","lineage":["https://openalex.org/I121296143"]},{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxun Liu","raw_affiliation_strings":["Hunan University of science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University of science and Technology","institution_ids":["https://openalex.org/I121296143","https://openalex.org/I49934816"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"24","first_page":"19853","last_page":"19861"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.4390999972820282,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.4390999972820282,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.3531999886035919,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.08709999918937683,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5985000133514404},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5982000231742859},{"id":"https://openalex.org/keywords/heterogeneous-network","display_name":"Heterogeneous network","score":0.5196999907493591},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3684000074863434},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.3424000144004822},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3206000030040741},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.3192000091075897}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8233000040054321},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5985000133514404},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5982000231742859},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.5196999907493591},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5072000026702881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3995000123977661},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3684000074863434},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C172430144","wikidata":"https://www.wikidata.org/wiki/Q17111997","display_name":"Symmetric multiprocessor system","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C2781357197","wikidata":"https://www.wikidata.org/wiki/Q5757597","display_name":"High memory","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2621999979019165},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25929999351501465}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i24.39068","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i24.39068","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39068/43030","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/39068","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/39068","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i24.39068","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i24.39068","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39068/43030","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.612442672252655}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329781","display_name":"Hubei University","ror":"https://ror.org/03a60m280"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138041567.pdf","grobid_xml":"https://content.openalex.org/works/W7138041567.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real-world":[0],"graphs":[1],"or":[2],"networks":[3,17],"are":[4],"usually":[5,42],"heterogeneous,":[6],"involving":[7],"multiple":[8],"types":[9],"of":[10,40,61,73,99,142,170],"nodes":[11,24],"and":[12,25,58,96,145,202],"relationships.":[13],"Heterogeneous":[14,87],"graph":[15,106,126,181],"neural":[16],"(HGNNs)":[18],"can":[19],"effectively":[20],"handle":[21],"these":[22],"diverse":[23],"edges,":[26],"capturing":[27],"heterogeneous":[28,105,153,172,180],"information":[29,122,154],"within":[30],"the":[31,70,85,94,110,115,140,152,171],"graph,":[32],"thus":[33],"exhibiting":[34],"outstanding":[35],"performance.":[36,116,190],"However,":[37],"most":[38],"methods":[39],"HGNNs":[41],"involve":[43],"complex":[44],"structural":[45],"designs,":[46],"leading":[47],"to":[48,108,138,164],"problems":[49],"such":[50],"as":[51],"high":[52],"memory":[53,200],"usage,":[54,201],"long":[55],"inference":[56],"time,":[57],"extensive":[59],"consumption":[60],"computing":[62,111],"resources.":[63],"These":[64],"limitations":[65],"pose":[66],"certain":[67],"challenges":[68],"for":[69,76],"practical":[71],"application":[72],"HGNNs,":[74],"especially":[75],"resource-constrained":[77],"devices.":[78],"To":[79],"mitigate":[80],"this":[81,193],"issue,":[82],"we":[83],"propose":[84],"Spiking":[86,100],"Graph":[88],"Attention":[89],"Networks":[90,102],"(SpikingHAN),":[91],"which":[92],"incorporates":[93],"brain-inspired":[95],"energy-saving":[97],"properties":[98],"Neural":[101],"(SNNs)":[103],"into":[104,155],"learning":[107],"reduce":[109],"cost":[112],"without":[113],"compromising":[114],"Specifically,":[117],"SpikingHAN":[118,185],"aggregates":[119],"metapath-based":[120],"neighbor":[121],"using":[123],"a":[124,134,156,166],"single-layer":[125],"convolution":[127],"with":[128,194],"shared":[129],"parameters.":[130],"It":[131,191],"then":[132],"employs":[133],"semantic-level":[135],"attention":[136],"mechanism":[137],"capture":[139],"importance":[141],"different":[143],"meta-paths":[144],"performs":[146],"semantic":[147],"aggregation.":[148],"Finally,":[149],"it":[150],"encodes":[151],"spike":[157],"sequence":[158],"through":[159],"SNNs,":[160],"simulating":[161],"bioinformatic":[162],"processing":[163],"derive":[165],"binarized":[167],"1-bit":[168],"representation":[169],"graph.":[173],"Comprehensive":[174],"experimental":[175],"results":[176],"from":[177],"three":[178],"real-world":[179],"datasets":[182],"show":[183],"that":[184],"delivers":[186],"competitive":[187],"node":[188],"classification":[189],"achieves":[192],"fewer":[195],"parameters,":[196],"quicker":[197],"inference,":[198],"reduced":[199],"lower":[203],"energy":[204],"consumption.":[205]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-18T00:00:00"}
