{"id":"https://openalex.org/W7163638007","doi":"https://doi.org/10.48550/arxiv.2606.05413","title":"CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting","display_name":"CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163638007","doi":"https://doi.org/10.48550/arxiv.2606.05413"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.05413","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05413","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.05413","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137921175","display_name":"Zhaoqi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhaoqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137945216","display_name":"Miao Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Miao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137964767","display_name":"Yi Li (1144)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137933237","display_name":"Linyou Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Linyou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137991760","display_name":"Siqiang Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Siqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137967842","display_name":"Gao Cong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cong, Gao","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":0.5983999967575073,"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":0.5983999967575073,"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":0.13750000298023224,"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":0.039500001817941666,"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/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.7179999947547913},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5275999903678894},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5120000243186951},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3901999890804291},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.34299999475479126},{"id":"https://openalex.org/keywords/urban-planning","display_name":"Urban planning","score":0.3319000005722046},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3149999976158142}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.7179999947547913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7084000110626221},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5275999903678894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5148000121116638},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5120000243186951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49219998717308044},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C49545453","wikidata":"https://www.wikidata.org/wiki/Q69883","display_name":"Urban planning","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3163999915122986},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.30059999227523804},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.05413","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05413","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.05413","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05413","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8404459357261658}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"urban":[1,21,61,104,186],"environments":[2],"continue":[3],"to":[4,55,79,127,143],"evolve":[5],"rapidly,":[6],"accurately":[7],"modeling":[8,91],"the":[9,48,57,81,163],"dynamic":[10],"behaviour":[11],"of":[12,14,60,85],"Points":[13],"Interest":[15],"is":[16,191],"essential":[17],"for":[18,185],"supporting":[19],"data-driven":[20],"planning":[22],"and":[23,43,53,95,130,135,140,146,174,182],"commercial":[24],"decision-making.":[25],"While":[26],"recent":[27],"advancements":[28],"in":[29,101,168],"spatio-temporal":[30,115,169],"graph":[31],"learning":[32,119],"have":[33],"improved":[34],"POI":[35,74],"forecasting,":[36,76,170],"most":[37],"methods":[38],"rely":[39],"on":[40,151],"proximity-based":[41],"graphs":[42,142],"correlation-driven":[44],"modeling,":[45,173],"which":[46,77],"overlook":[47],"functional":[49,96],"dependencies":[50],"between":[51,133],"POIs":[52,100],"fail":[54],"capture":[56],"causal":[58,117,175],"effects":[59],"interventions.":[62],"In":[63],"this":[64],"paper,":[65],"we":[66,111],"introduce":[67],"a":[68,86,102,114,179],"novel":[69],"research":[70],"problem":[71],"--":[72],"cold-start":[73],"check-in":[75,83],"aims":[78],"predict":[80],"future":[82],"pattern":[84],"newly":[87],"introduced":[88],"POI,":[89],"by":[90],"its":[92,166],"temporal":[93],"evolution":[94],"interactions":[97],"with":[98],"nearby":[99],"structured":[103],"spatial":[105,131],"context.":[106],"To":[107],"address":[108],"these":[109],"challenges,":[110],"propose":[112],"CausalPOI,":[113],"graph-based":[116],"representation":[118],"framework.":[120],"CausalPOI":[121,157],"leverages":[122],"Spatio-Temporal":[123],"Functional":[124],"Interaction":[125],"Graph":[126],"model":[128],"semantic":[129,171],"relationships":[132],"POIs,":[134],"constructs":[136],"structurally":[137],"aligned":[138],"treatment":[139],"control":[141],"simulate":[144],"factual":[145],"counterfactual":[147],"scenarios.":[148],"Extensive":[149],"experiments":[150],"real-world":[152],"SafeGraph":[153],"datasets":[154],"demonstrate":[155],"that":[156],"significantly":[158],"outperforms":[159],"state-of-the-art":[160],"baselines":[161],"across":[162],"board,":[164],"validating":[165],"effectiveness":[167],"interaction":[172],"effect":[176],"estimation,":[177],"providing":[178],"more":[180],"interpretable":[181],"actionable":[183],"foundation":[184],"intervention":[187],"analysis.":[188],"Source":[189],"code":[190],"available":[192],"at":[193],"Github.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-06T00:00:00"}
