{"id":"https://openalex.org/W7151752229","doi":"https://doi.org/10.48550/arxiv.2604.05254","title":"EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks","display_name":"EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7151752229","doi":"https://doi.org/10.48550/arxiv.2604.05254"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05254","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05254","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05254","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123531258","display_name":"Zhiming Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Zhiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019309926","display_name":"Meimei Huo","orcid":"https://orcid.org/0000-0001-7493-340X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huo, Menghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133148252","display_name":"Yujue Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yujue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.10040000081062317,"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":0.10040000081062317,"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/T11864","display_name":"Supply Chain Resilience and Risk Management","score":0.09520000219345093,"subfield":{"id":"https://openalex.org/subfields/1408","display_name":"Strategy and Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.05640000104904175,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/timestamp","display_name":"Timestamp","score":0.6486999988555908},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5072000026702881},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.42170000076293945},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.4196999967098236},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.37470000982284546},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.3734999895095825},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.37279999256134033},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.36550000309944153},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.32499998807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7177000045776367},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.6486999988555908},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5072000026702881},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47209998965263367},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.42170000076293945},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.4196999967098236},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37040001153945923},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36730000376701355},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3619000017642975},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.32499998807907104},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.30399999022483826},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C2778397037","wikidata":"https://www.wikidata.org/wiki/Q17083947","display_name":"Supply chain network","level":4,"score":0.2653999924659729},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C104954878","wikidata":"https://www.wikidata.org/wiki/Q1648707","display_name":"Routing protocol","level":3,"score":0.25600001215934753},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05254","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05254","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05254","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05254","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.42178836464881897}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"logistics":[1],"networks":[2],"generate":[3],"rich":[4],"operational":[5],"data":[6],"streams":[7],"at":[8],"every":[9],"warehouse":[10],"node":[11],"and":[12,19,96,135,174],"transportation":[13],"lane":[14],"--":[15,25,156,166],"from":[16],"order":[17],"timestamps":[18],"routing":[20],"records":[21],"to":[22],"shipping":[23],"manifests":[24],"yet":[26],"predicting":[27],"delivery":[28],"delays":[29],"remains":[30],"predominantly":[31],"reactive.":[32],"Existing":[33],"predictive":[34,172],"approaches":[35],"typically":[36],"treat":[37],"this":[38,67],"problem":[39],"either":[40],"as":[41,50],"a":[42,51,71,91,109,148,157],"tabular":[43],"classification":[44],"task,":[45,55],"ignoring":[46],"network":[47],"topology,":[48],"or":[49],"time-series":[52],"anomaly":[53],"detection":[54],"overlooking":[56],"the":[57,61,115,145,162,168],"spatial":[58],"dependencies":[59,99],"of":[60,133,138,153,171],"supply":[62,78],"chain":[63,79],"graph.":[64],"To":[65],"bridge":[66],"gap,":[68],"we":[69],"propose":[70],"hybrid":[72],"deep":[73],"learning":[74,111],"framework":[75,123,146],"for":[76],"proactive":[77],"risk":[80],"management.":[81],"The":[82],"proposed":[83],"method":[84],"jointly":[85],"models":[86],"temporal":[87],"order-flow":[88],"dynamics":[89],"via":[90,108],"lightweight":[92],"Transformer":[93],"patch":[94],"encoder":[95],"inter-hub":[97],"relational":[98],"through":[100],"an":[101,131,136],"Edge-Aware":[102],"Graph":[103],"Attention":[104],"Network":[105],"(E-GAT),":[106],"optimized":[107],"multi-task":[110],"objective.":[112],"Evaluated":[113],"on":[114],"real-world":[116],"DataCo":[117],"Smart":[118],"Supply":[119],"Chain":[120],"dataset,":[121],"our":[122],"achieves":[124],"consistent":[125],"improvements":[126],"over":[127,161],"baseline":[128],"methods,":[129],"yielding":[130],"F1-score":[132],"0.8762":[134],"AUC-ROC":[137],"0.9773.":[139],"Across":[140],"four":[141],"independent":[142],"random":[143],"seeds,":[144],"exhibits":[147],"cross-seed":[149],"F1":[150],"standard":[151],"deviation":[152],"only":[154],"0.0089":[155],"3.8":[158],"times":[159],"improvement":[160],"best":[163],"ablated":[164],"variant":[165],"achieving":[167],"strongest":[169],"balance":[170],"accuracy":[173],"training":[175],"stability":[176],"among":[177],"all":[178],"evaluated":[179],"models.":[180]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-09T00:00:00"}