{"id":"https://openalex.org/W4319167014","doi":"https://doi.org/10.1145/3539597.3570409","title":"Inductive Graph Transformer for Delivery Time Estimation","display_name":"Inductive Graph Transformer for Delivery Time Estimation","publication_year":2023,"publication_date":"2023-02-22","ids":{"openalex":"https://openalex.org/W4319167014","doi":"https://doi.org/10.1145/3539597.3570409"},"language":"en","primary_location":{"id":"doi:10.1145/3539597.3570409","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539597.3570409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"preprint","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/A5060375325","display_name":"Xin Zhou","orcid":"https://orcid.org/0000-0003-0948-8033"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Xin Zhou","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062638093","display_name":"Jinglong Wang","orcid":"https://orcid.org/0000-0003-1678-931X"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinglong Wang","raw_affiliation_strings":["Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072988055","display_name":"Yong Liu","orcid":"https://orcid.org/0000-0001-9031-9696"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yong Liu","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101768800","display_name":"Xingyu Wu","orcid":"https://orcid.org/0000-0001-7802-0931"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingyu Wu","raw_affiliation_strings":["Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101789458","display_name":"Zhiqi Shen","orcid":"https://orcid.org/0000-0001-7626-7295"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhiqi Shen","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027298172","display_name":"Cyril Leung","orcid":"https://orcid.org/0000-0001-9911-2069"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Cyril Leung","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5060375325"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":2.317,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.86580916,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"679","last_page":"687"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9983000159263611,"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":0.9983000159263611,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9958000183105469,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9882000088691711,"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":0.7114234566688538},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5930355787277222},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5754784941673279},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4899432063102722},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46259942650794983},{"id":"https://openalex.org/keywords/purchasing","display_name":"Purchasing","score":0.44663307070732117},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3619048297405243},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3442927300930023},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2934883236885071},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13456496596336365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7114234566688538},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5930355787277222},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5754784941673279},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4899432063102722},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46259942650794983},{"id":"https://openalex.org/C2778813691","wikidata":"https://www.wikidata.org/wiki/Q1369832","display_name":"Purchasing","level":2,"score":0.44663307070732117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3619048297405243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3442927300930023},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2934883236885071},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13456496596336365},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3539597.3570409","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539597.3570409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1563938429","https://openalex.org/W2157331557","https://openalex.org/W2295598076","https://openalex.org/W2793768763","https://openalex.org/W2809128166","https://openalex.org/W2809623940","https://openalex.org/W2904628589","https://openalex.org/W2910952060","https://openalex.org/W2912083425","https://openalex.org/W2945996535","https://openalex.org/W2962946486","https://openalex.org/W2964051675","https://openalex.org/W2970066309","https://openalex.org/W3012871709","https://openalex.org/W3019863187","https://openalex.org/W3026076535","https://openalex.org/W3029687524","https://openalex.org/W3035050855","https://openalex.org/W3043708151","https://openalex.org/W3047219565","https://openalex.org/W3080227975","https://openalex.org/W3080344546","https://openalex.org/W3080548826","https://openalex.org/W3080590811","https://openalex.org/W3080902222","https://openalex.org/W3081469395","https://openalex.org/W3104030692","https://openalex.org/W3117136530","https://openalex.org/W6600129504","https://openalex.org/W6767098714"],"related_works":["https://openalex.org/W2351217280","https://openalex.org/W2086836292","https://openalex.org/W2354083748","https://openalex.org/W2357949222","https://openalex.org/W2338005294","https://openalex.org/W2104192539","https://openalex.org/W4206178588","https://openalex.org/W3094491777","https://openalex.org/W3214715529","https://openalex.org/W4287635093"],"abstract_inverted_index":{"Providing":[0],"accurate":[1],"estimated":[2,35,77],"time":[3,36,78],"of":[4,15,37,67,90,98,223],"package":[5,122],"delivery":[6,123,224],"on":[7,206,221],"users'":[8],"purchasing":[9,20],"pages":[10],"for":[11,57],"e-commerce":[12],"platforms":[13],"is":[14,41],"great":[16],"importance":[17],"to":[18,54,120,198],"their":[19],"decisions":[21],"and":[22,62,116,136,153,176,187],"post-purchase":[23],"experiences.":[24],"Although":[25],"this":[26,102],"problem":[27],"shares":[28],"some":[29],"common":[30],"issues":[31],"with":[32,44,59,82],"the":[33,45,73,76,87,95,146,168,177,192,196,218],"conventional":[34],"arrival":[38],"(ETA),":[39],"it":[40],"more":[42],"challenging":[43],"following":[46],"aspects:":[47],"1)":[48],"Inductive":[49],"inference.":[50],"Models":[51],"are":[52],"required":[53],"predict":[55],"ETA":[56],"orders":[58],"unseen":[60],"retailers":[61],"addresses;":[63],"2)":[64],"High-order":[65],"interaction":[66,97],"order":[68],"semantic":[69],"information.":[70],"Apart":[71],"from":[72,126,149],"spatio-temporal":[74],"features,":[75],"also":[79],"varies":[80],"greatly":[81],"other":[83],"factors,":[84],"such":[85],"as":[86,92,94,139],"packaging":[88],"efficiency":[89],"retailers,":[91],"well":[93],"high-order":[96],"these":[99],"factors.":[100],"In":[101,163],"paper,":[103],"we":[104,165],"propose":[105],"an":[106],"inductive":[107],"graph":[108,118,128,159],"transformer":[109,129,138],"(IGT)":[110],"that":[111,143,211],"leverages":[112],"raw":[113,151],"feature":[114,152],"information":[115,148,189],"structural":[117],"data":[119],"estimate":[121],"time.":[124,225],"Different":[125],"previous":[127],"architectures,":[130],"IGT":[131,197],"adopts":[132],"a":[133,140,158],"decoupled":[134],"pipeline":[135],"trains":[137],"regression":[141],"function":[142],"can":[144,215],"capture":[145],"multiplex":[147],"both":[150],"dense":[154],"embeddings":[155],"encoded":[156],"by":[157,171],"neural":[160],"network":[161],"(GNN).":[162],"addition,":[164],"further":[166],"simplify":[167],"GNN":[169,194],"structure":[170],"removing":[172],"its":[173],"non-linear":[174],"activation":[175],"learnable":[178],"linear":[179,188],"transformation":[180],"matrix.":[181],"The":[182],"reduced":[183],"parameter":[184],"search":[185],"space":[186],"propagation":[190],"in":[191,201],"simplified":[193],"enable":[195],"be":[199],"applied":[200],"large-scale":[202],"industrial":[203],"scenarios.":[204],"Experiments":[205],"real-world":[207],"logistics":[208],"datasets":[209],"show":[210],"our":[212],"proposed":[213],"model":[214],"significantly":[216],"outperform":[217],"state-of-the-art":[219],"methods":[220],"estimation":[222]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}