{"id":"https://openalex.org/W4402351505","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650980","title":"HyperAggregation: Aggregating over Graph Edges with Hypernetworks","display_name":"HyperAggregation: Aggregating over Graph Edges with Hypernetworks","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351505","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650980"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650980","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030641477","display_name":"Nicolas Lell","orcid":"https://orcid.org/0000-0002-6079-6480"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Nicolas Lell","raw_affiliation_strings":["Ulm University,Ulm,Germany"],"affiliations":[{"raw_affiliation_string":"Ulm University,Ulm,Germany","institution_ids":["https://openalex.org/I196349391"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005533034","display_name":"Ansgar Scherp","orcid":"https://orcid.org/0000-0002-2653-9245"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ansgar Scherp","raw_affiliation_strings":["Ulm University,Ulm,Germany"],"affiliations":[{"raw_affiliation_string":"Ulm University,Ulm,Germany","institution_ids":["https://openalex.org/I196349391"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030641477"],"corresponding_institution_ids":["https://openalex.org/I196349391"],"apc_list":null,"apc_paid":null,"fwci":0.3626,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66419137,"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":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9986000061035156,"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":0.9986000061035156,"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/T10720","display_name":"Complexity and Algorithms in Graphs","score":0.9972000122070312,"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"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9970999956130981,"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":0.5661890506744385},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4736289381980896},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.22763195633888245}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5661890506744385},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4736289381980896},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.22763195633888245}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650980","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1757990252","https://openalex.org/W2153959628","https://openalex.org/W2154851992","https://openalex.org/W2529996553","https://openalex.org/W3104097132","https://openalex.org/W3160872503","https://openalex.org/W4281765589","https://openalex.org/W4292952789","https://openalex.org/W4377130469","https://openalex.org/W4385488539","https://openalex.org/W4385570397","https://openalex.org/W4385767704","https://openalex.org/W4387322877","https://openalex.org/W4401548252","https://openalex.org/W6602764823","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6745537798","https://openalex.org/W6752110883","https://openalex.org/W6755501315","https://openalex.org/W6756561102","https://openalex.org/W6760001035","https://openalex.org/W6765543928","https://openalex.org/W6770147432","https://openalex.org/W6771621015","https://openalex.org/W6772776185","https://openalex.org/W6774097426","https://openalex.org/W6776072314","https://openalex.org/W6776488958","https://openalex.org/W6779961489","https://openalex.org/W6784333009","https://openalex.org/W6794579267","https://openalex.org/W6795140394","https://openalex.org/W6796549732","https://openalex.org/W6796694783","https://openalex.org/W6801851208","https://openalex.org/W6802881785","https://openalex.org/W6803202227","https://openalex.org/W6803996784","https://openalex.org/W6804049574","https://openalex.org/W6810465265","https://openalex.org/W6839642780","https://openalex.org/W6841989129","https://openalex.org/W6848269516","https://openalex.org/W6849797272","https://openalex.org/W6853380566","https://openalex.org/W6856825821","https://openalex.org/W6864424756"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"HyperAggregation":[0,52,97,164],"is":[1,39,57,65,119,172],"a":[2,12,58,68,72,133],"hypernetwork-based":[3,73],"aggregation":[4,34,74],"function":[5],"for":[6,83,102],"Graph":[7],"Neural":[8],"Networks.":[9],"It":[10],"uses":[11],"hyper-network":[13],"to":[14,29,168],"dynamically":[15],"generate":[16],"weights":[17,38],"in":[18,53,107,122,146],"the":[19,22,36,84,123,127,137,140,155],"size":[20],"of":[21,136,163],"current":[23],"neighborhood,":[24],"which":[25],"are":[26],"then":[27],"used":[28,101],"aggregate":[30],"this":[31],"neighborhood.":[32],"This":[33],"with":[35,71,148],"generated":[37],"done":[40],"like":[41],"an":[42],"MLP-Mixer":[43,62],"channel":[44],"mixing":[45],"over":[46],"variable-sized":[47],"vertex":[48,85],"neighborhoods.":[49],"We":[50,76],"demonstrate":[51],"two":[54],"models,":[55],"GraphHyperMixer":[56,117],"model":[59],"based":[60],"on":[61,79],"while":[63],"GraphHyperConv":[64,113],"derived":[66],"from":[67],"GCN":[69],"but":[70],"function.":[75],"perform":[77,145],"experiments":[78,171],"diverse":[80],"benchmark":[81],"datasets":[82,106],"classification,":[86,88],"graph":[87,90],"and":[89,104,110,118],"regression":[91],"tasks.":[92],"The":[93,161],"results":[94],"show":[95],"that":[96],"can":[98],"be":[99],"effectively":[100],"homophilic":[103],"heterophilic":[105,128],"both":[108],"inductive":[109],"transductive":[111,124],"settings.":[112],"performs":[114],"better":[115],"than":[116],"especially":[120],"strong":[121],"setting.":[125],"On":[126,139],"dataset":[129],"Roman-Empire":[130],"it":[131],"reaches":[132],"new":[134],"state":[135],"art.":[138],"graph-level":[141],"tasks":[142],"our":[143],"models":[144],"line":[147],"similarly":[149],"sized":[150],"models.":[151],"Ablation":[152],"studies":[153],"investigate":[154],"robustness":[156],"against":[157],"various":[158],"hyperparameter":[159],"choices.":[160],"implementation":[162],"as":[165],"well":[166],"code":[167],"reproduce":[169],"all":[170],"available":[173],"under":[174],"https://github.com/Foisunt/HyperAggregation.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}