{"id":"https://openalex.org/W4290877193","doi":"https://doi.org/10.1145/3534678.3539422","title":"Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning","display_name":"Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290877193","doi":"https://doi.org/10.1145/3534678.3539422"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539422","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539422","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5065420012","display_name":"Rongfan Li","orcid":"https://orcid.org/0000-0001-8055-8909"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Rongfan Li","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034789908","display_name":"Ting Zhong","orcid":"https://orcid.org/0000-0002-8163-3146"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Zhong","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056532017","display_name":"Xinke Jiang","orcid":"https://orcid.org/0000-0003-1286-7974"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinke Jiang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086447943","display_name":"Goce Trajcevski","orcid":"https://orcid.org/0000-0002-8839-6278"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Goce Trajcevski","raw_affiliation_strings":["Iowa State University, Ames, IA, USA"],"affiliations":[{"raw_affiliation_string":"Iowa State University, Ames, IA, USA","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078883186","display_name":"Jin Wu","orcid":"https://orcid.org/0000-0001-5930-4170"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Wu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100403505","display_name":"Fan Zhou","orcid":"https://orcid.org/0000-0002-8038-8150"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Zhou","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5065420012"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":15.1167,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.99471545,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"936","last_page":"944"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9927999973297119,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9912999868392944,"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/computer-science","display_name":"Computer science","score":0.7644179463386536},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5601791143417358},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5069668292999268},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.48143619298934937},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4791826009750366},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4538506269454956},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.42077669501304626}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7644179463386536},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5601791143417358},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5069668292999268},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.48143619298934937},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4791826009750366},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4538506269454956},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.42077669501304626},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539422","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539422","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7099999785423279}],"awards":[{"id":"https://openalex.org/G5383046524","display_name":null,"funder_award_id":"62176043, 62072077","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G785087892","display_name":null,"funder_award_id":"SWIFT No.2030249","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2123958887","https://openalex.org/W2549975402","https://openalex.org/W2756203131","https://openalex.org/W2903871660","https://openalex.org/W2904832339","https://openalex.org/W2996847713","https://openalex.org/W2998436408","https://openalex.org/W3012562343","https://openalex.org/W3012816161","https://openalex.org/W3034781633","https://openalex.org/W3035248757","https://openalex.org/W3036446966","https://openalex.org/W3093761440","https://openalex.org/W3099152386","https://openalex.org/W3103427490","https://openalex.org/W3103720336","https://openalex.org/W3105319189","https://openalex.org/W3170140111","https://openalex.org/W3174022889","https://openalex.org/W3175872245","https://openalex.org/W3175926635","https://openalex.org/W6729089581"],"related_works":["https://openalex.org/W2770593030","https://openalex.org/W3154990682","https://openalex.org/W2560201613","https://openalex.org/W2171975302","https://openalex.org/W2022352247","https://openalex.org/W2488129135","https://openalex.org/W4312219546","https://openalex.org/W2377538627","https://openalex.org/W4386136067","https://openalex.org/W4286858940"],"abstract_inverted_index":{"Modeling":[0],"complex":[1,56],"spatial":[2],"and":[3,22,40,82,85,116,124,142,165,183],"temporal":[4,23],"dependencies":[5],"are":[6,43,48,118],"indispensable":[7],"for":[8,135],"location-bound":[9],"time":[10],"series":[11],"learning.":[12],"Existing":[13],"methods,":[14],"typically":[15],"relying":[16],"on":[17,27,54,175],"graph":[18,63,89,130,134],"neural":[19,29],"networks":[20],"(GNNs)":[21],"learning":[24,157,179],"modules":[25],"based":[26],"recurrent":[28],"networks,":[30],"have":[31],"achieved":[32],"significant":[33],"performance":[34,190],"improvements.":[35],"However,":[36],"their":[37],"representation":[38],"capabilities":[39],"prediction":[41],"results":[42],"limited":[44],"when":[45],"pre-defined":[46],"graphs":[47],"unavailable.":[49],"Unlike":[50],"spatio-temporal":[51,156,178],"GNNs":[52],"focusing":[53],"designing":[55],"architectures,":[57],"we":[58],"propose":[59],"a":[60,91,151],"novel":[61],"adaptive":[62],"construction":[64],"strategy:":[65],"Self-Paced":[66],"Graph":[67],"Contrastive":[68],"Learning":[69],"(SPGCL).":[70],"It":[71],"learns":[72],"informative":[73],"relations":[74],"by":[75],"maximizing":[76],"the":[77,95,104,113,122,127,132,137,143,188,194],"distinguishing":[78],"margin":[79],"between":[80,140],"positive":[81],"negative":[83],"neighbors":[84,110,145],"generates":[86],"an":[87],"optimal":[88],"with":[90,103],"self-paced":[92,129],"strategy.":[93],"Specifically,":[94],"existing":[96],"neighborhoods":[97],"iteratively":[98],"absorb":[99],"more":[100],"reliable":[101],"nodes":[102,141],"highest":[105],"affinity":[106],"scores":[107],"as":[108],"new":[109,152],"to":[111,120,169],"generate":[112],"next-round":[114],"neighborhoods,":[115],"augmentations":[117],"applied":[119],"improve":[121],"transferability":[123],"robustness.":[125],"As":[126],"adaptively":[128],"approaches":[131],"optimized":[133],"prediction,":[136],"mutual":[138],"information":[139,160],"corresponding":[144],"is":[146],"maximized.":[147],"Our":[148],"work":[149],"provides":[150],"perspective":[153],"of":[154,191],"addressing":[155],"problems":[158],"beyond":[159],"aggregation":[161],"in":[162],"Euclidean":[163],"space":[164],"can":[166],"be":[167],"generalized":[168],"different":[170],"tasks.":[171],"Extensive":[172],"experiments":[173],"conducted":[174],"two":[176],"typical":[177],"tasks":[180],"(traffic":[181],"forecasting":[182],"land":[184],"displacement":[185],"prediction)":[186],"demonstrate":[187],"superior":[189],"SPGCL":[192],"against":[193],"state-of-the-art.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":5}],"updated_date":"2026-03-29T08:15:47.926485","created_date":"2025-10-10T00:00:00"}
