{"id":"https://openalex.org/W7118665100","doi":"https://doi.org/10.48550/arxiv.2601.00430","title":"Toward Better Temporal Structures for Geopolitical Events Forecasting","display_name":"Toward Better Temporal Structures for Geopolitical Events Forecasting","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7118665100","doi":"https://doi.org/10.48550/arxiv.2601.00430"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.00430","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00430","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.00430","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076242590","display_name":"Kian Ahrabian","orcid":"https://orcid.org/0000-0002-6606-9735"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ahrabian, Kian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119844408","display_name":"Eric Boxer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boxer, Eric","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073443117","display_name":"Jay Pujara","orcid":"https://orcid.org/0000-0001-6921-1744"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pujara, Jay","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5076242590"],"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.8159000277519226,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.8159000277519226,"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"}},{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.01759999990463257,"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"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.01549999974668026,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/geopolitics","display_name":"Geopolitics","score":0.6836000084877014},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.6424999833106995},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6017000079154968},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49459999799728394},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.44429999589920044},{"id":"https://openalex.org/keywords/through-the-lens-metering","display_name":"Through-the-lens metering","score":0.42739999294281006},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4203000068664551}],"concepts":[{"id":"https://openalex.org/C201960208","wikidata":"https://www.wikidata.org/wiki/Q159385","display_name":"Geopolitics","level":3,"score":0.6836000084877014},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.6424999833106995},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6017000079154968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5810999870300293},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49459999799728394},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.44429999589920044},{"id":"https://openalex.org/C43091099","wikidata":"https://www.wikidata.org/wiki/Q1067788","display_name":"Through-the-lens metering","level":3,"score":0.42739999294281006},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4203000068664551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41449999809265137},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.38019999861717224},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C195818886","wikidata":"https://www.wikidata.org/wiki/Q5421724","display_name":"Expressive power","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27390000224113464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2685000002384186},{"id":"https://openalex.org/C106306483","wikidata":"https://www.wikidata.org/wiki/Q183984","display_name":"Futures contract","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2597000002861023}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.00430","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00430","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.00430","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00430","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Forecasting":[0],"on":[1,140],"geopolitical":[2,115],"temporal":[3,25,36,67],"knowledge":[4,26],"graphs":[5,27],"(TKGs)":[6],"through":[7],"the":[8,40,50,123,128,147,152],"lens":[9],"of":[10,49,53,58,86,110,150],"large":[11],"language":[12],"models":[13],"(LLMs)":[14],"has":[15],"recently":[16],"gained":[17],"traction.":[18],"While":[19],"TKGs":[20],"and":[21,136,159,163],"their":[22,102],"generalization,":[23],"hyper-relational":[24],"(HTKGs),":[28],"offer":[29],"a":[30,56,84,97],"straightforward":[31],"structure":[32],"to":[33,43,156],"represent":[34],"simple":[35],"relationships,":[37],"they":[38],"lack":[39,57],"expressive":[41],"power":[42],"convey":[44],"complex":[45,108,166],"facts":[46,111],"efficiently.":[47],"One":[48],"critical":[51],"limitations":[52],"HTKGs":[54],"is":[55],"support":[59],"for":[60,99],"more":[61],"than":[62],"two":[63,107],"primary":[64],"entities":[65],"in":[66,72,79,114,165],"facts,":[68],"which":[69],"commonly":[70,112],"occur":[71],"real-world":[73],"events.":[74],"To":[75],"address":[76],"this":[77,80,119],"limitation,":[78],"work,":[81],"we":[82,121,134],"study":[83],"generalization":[85],"HTKGs,":[87],"Hyper-Relational":[88],"Temporal":[89],"Knowledge":[90],"Generalized":[91],"Hypergraphs":[92],"(HTKGHs).":[93],"We":[94],"first":[95],"derive":[96],"formalization":[98,154],"HTKGHs,":[100],"demonstrating":[101],"backward":[103],"compatibility":[104],"while":[105],"supporting":[106],"types":[109],"found":[113],"incidents.":[116],"Then,":[117],"utilizing":[118,151],"formalization,":[120],"introduce":[122],"htkgh-polecat":[124],"dataset,":[125,142],"built":[126],"upon":[127],"global":[129],"event":[130],"database":[131],"POLECAT.":[132],"Finally,":[133],"benchmark":[135],"analyze":[137],"popular":[138],"LLMs":[139],"our":[141],"providing":[143],"insights":[144],"into":[145],"1)":[146],"positive":[148],"impact":[149],"HTKGH":[153],"compared":[155],"existing":[157],"ones":[158],"2)":[160],"LLMs'":[161],"adaptability":[162],"capabilities":[164],"forecasting":[167],"tasks.":[168]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-01-08T00:00:00"}