{"id":"https://openalex.org/W4401856675","doi":"https://doi.org/10.1145/3637528.3671912","title":"Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks","display_name":"Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401856675","doi":"https://doi.org/10.1145/3637528.3671912"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671912","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671912","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671912","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671912","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011662141","display_name":"Wenying Duan","orcid":"https://orcid.org/0000-0002-2515-7800"},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenying Duan","raw_affiliation_strings":["Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction, Nanchang University, Nanchang, Jiangxi, China"],"affiliations":[{"raw_affiliation_string":"Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction, Nanchang University, Nanchang, Jiangxi, China","institution_ids":["https://openalex.org/I141649914"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111300794","display_name":"Tianxiang Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianxiang Fang","raw_affiliation_strings":["Nanchang University, Nanchang, China"],"affiliations":[{"raw_affiliation_string":"Nanchang University, Nanchang, China","institution_ids":["https://openalex.org/I141649914"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100813758","display_name":"Hong Rao","orcid":null},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Rao","raw_affiliation_strings":["School of Software, Nanchang University, Nanchang, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Nanchang University, Nanchang, China","institution_ids":["https://openalex.org/I141649914"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101689897","display_name":"Xiaoxi He","orcid":"https://orcid.org/0009-0001-1178-263X"},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Xiaoxi He","raw_affiliation_strings":["Faculty of Science and Technology, University of Macau, Macau, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, University of Macau, Macau, China","institution_ids":["https://openalex.org/I204512498"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011662141"],"corresponding_institution_ids":["https://openalex.org/I141649914"],"apc_list":null,"apc_paid":null,"fwci":0.2653,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55643581,"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":"701","last_page":"712"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7399080991744995},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5821813941001892},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4543730914592743},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43016698956489563},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4118034839630127},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.293684720993042},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0729181170463562}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7399080991744995},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5821813941001892},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4543730914592743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43016698956489563},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4118034839630127},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.293684720993042},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0729181170463562},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671912","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671912","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671912","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671912","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671912","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671912","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401856675.pdf"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1997315949","https://openalex.org/W2560674852","https://openalex.org/W2756203131","https://openalex.org/W2907492528","https://openalex.org/W2963076818","https://openalex.org/W3035580605","https://openalex.org/W3103720336","https://openalex.org/W3126367810","https://openalex.org/W3170140111","https://openalex.org/W3175505246","https://openalex.org/W3185561982","https://openalex.org/W4283796372","https://openalex.org/W4283817628","https://openalex.org/W4312703862","https://openalex.org/W4382449675","https://openalex.org/W4385562696","https://openalex.org/W4389692353","https://openalex.org/W4391640582","https://openalex.org/W4393147339"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W230091440","https://openalex.org/W2390279801","https://openalex.org/W2233261550","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2376932109"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,50,160],"present":[4],"a":[5,40,45,137,203],"novel":[6],"method":[7],"to":[8,48,83,112,116],"significantly":[9,79],"enhance":[10],"the":[11,23,26,33,67,88,93,131,147,163,166,169,184,194,197,208],"computational":[12,59,71,122],"efficiency":[13],"of":[14,25,73,144,165,171,186,196,210],"Adaptive":[15],"Spatial-Temporal":[16],"Graph":[17,27],"Neural":[18],"Networks":[19],"(ASTGNNs)":[20],"by":[21,91],"introducing":[22],"concept":[24],"Winning":[28],"Ticket":[29,35],"(GWT),":[30],"derived":[31],"from":[32,81,168],"Lottery":[34],"Hypothesis":[36],"(LTH).":[37],"By":[38],"adopting":[39],"pre-determined":[41],"star":[42],"topology":[43],"as":[44],"GWT":[46,167],"prior":[47],"training,":[49,97],"balance":[51],"edge":[52],"reduction":[53],"with":[54,119,141],"efficient":[55,187],"information":[56],"propagation,":[57],"reducing":[58],"demands":[60],"while":[61],"maintaining":[62],"high":[63],"model":[64],"performance.":[65,158],"Both":[66],"time":[68],"and":[69,99,102,154],"memory":[70],"complexity":[72],"generating":[74],"adaptive":[75],"spatial-temporal":[76,134],"graphs":[77],"is":[78,110,215],"reduced":[80],"O(N2)":[82],"O(N).":[84],"Our":[85],"approach":[86,126],"streamlines":[87],"ASTGNN":[89],"deployment":[90],"eliminating":[92],"need":[94],"for":[95],"exhaustive":[96],"pruning,":[98],"retraining":[100],"cycles,":[101],"demonstrates":[103],"empirically":[104],"across":[105],"various":[106],"datasets":[107],"that":[108],"it":[109],"possible":[111],"achieve":[113],"comparable":[114],"performance":[115],"full":[117],"models":[118],"substantially":[120],"lower":[121],"costs.":[123],"Specifically,":[124],"our":[125],"enables":[127],"training":[128,153],"ASTGNNs":[129,190],"on":[130],"largest":[132],"scale":[133],"dataset":[135],"using":[136],"single":[138],"A6000":[139],"equipped":[140],"48":[142],"GB":[143],"memory,":[145],"overcoming":[146],"out-of-memory":[148],"issue":[149],"encountered":[150],"during":[151],"original":[152],"even":[155],"achieving":[156],"state-of-the-art":[157],"Furthermore,":[159],"delve":[161],"into":[162],"effectiveness":[164],"perspective":[170],"spectral":[172],"graph":[173,211],"theory,":[174],"providing":[175],"substantial":[176],"theoretical":[177],"support.":[178],"This":[179],"advancement":[180],"not":[181],"only":[182],"proves":[183],"existence":[185],"sub-networks":[188],"within":[189],"but":[191],"also":[192],"broadens":[193],"applicability":[195],"LTH":[198],"in":[199,207],"resource-constrained":[200],"settings,":[201],"marking":[202],"significant":[204],"step":[205],"forward":[206],"field":[209],"neural":[212],"networks.":[213],"Code":[214],"available":[216],"at":[217],"https://anonymous.4open.science/r/paper-1430.":[218]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}