{"id":"https://openalex.org/W7164026262","doi":"https://doi.org/10.48550/arxiv.2606.08306","title":"Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks","display_name":"Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks","publication_year":2026,"publication_date":"2026-06-06","ids":{"openalex":"https://openalex.org/W7164026262","doi":"https://doi.org/10.48550/arxiv.2606.08306"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08306","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.2606.08306","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138238257","display_name":"Micha\u0142 Czuba","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Czuba, Micha\u0142","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124969980","display_name":"Mateusz Stolarski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stolarski, Mateusz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092491305","display_name":"Adam Pir\u00f3g","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pir\u00f3g, Adam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039269881","display_name":"Piotr Bielak","orcid":"https://orcid.org/0000-0002-1487-2569"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bielak, Piotr","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138220450","display_name":"Piotr Br\u00f3dka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Br\u00f3dka, Piotr","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/T11273","display_name":"Advanced Graph Neural Networks","score":0.8712000250816345,"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":0.8712000250816345,"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":0.08429999649524689,"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"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.006399999838322401,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/heuristics","display_name":"Heuristics","score":0.7245000004768372},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.5667999982833862},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5382999777793884},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4634000062942505},{"id":"https://openalex.org/keywords/dynamics","display_name":"Dynamics (music)","score":0.39989998936653137},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.39070001244544983},{"id":"https://openalex.org/keywords/complex-network","display_name":"Complex network","score":0.37540000677108765},{"id":"https://openalex.org/keywords/network-dynamics","display_name":"Network dynamics","score":0.36039999127388},{"id":"https://openalex.org/keywords/network-model","display_name":"Network model","score":0.33410000801086426}],"concepts":[{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.7245000004768372},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6854000091552734},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.5667999982833862},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5382999777793884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5335999727249146},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.47749999165534973},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4634000062942505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40290001034736633},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C123757187","wikidata":"https://www.wikidata.org/wiki/Q9195957","display_name":"Network dynamics","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C36647736","wikidata":"https://www.wikidata.org/wiki/Q5418752","display_name":"Evolving networks","level":3,"score":0.31869998574256897},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C2993776861","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Open domain","level":3,"score":0.2906999886035919},{"id":"https://openalex.org/C124978682","wikidata":"https://www.wikidata.org/wiki/Q1201019","display_name":"Proof of concept","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C2987015589","wikidata":"https://www.wikidata.org/wiki/Q1040098","display_name":"Learning network","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08306","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.2606.08306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08306","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":"Good health and well-being","score":0.8003862500190735,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Network":[0],"dynamics":[1],"-":[2,10],"including":[3],"spreading,":[4],"influence":[5],"maximisation,":[6],"and":[7,26,53,84,90,121],"epidemic":[8],"modelling":[9],"remain":[11],"largely":[12],"confined":[13],"to":[14,78],"the":[15],"transductive":[16,91],"paradigm,":[17],"where":[18],"models":[19],"are":[20],"trained":[21,68],"on":[22,30,70,93,99],"a":[23,42,62],"single":[24],"network":[25,112],"cannot":[27],"be":[28],"reused":[29],"unseen":[31],"graphs":[32],"without":[33],"retraining.":[34],"We":[35],"argue":[36],"that":[37],"inductive":[38],"cross-network":[39],"generalisation":[40,77],"is":[41],"necessary":[43],"prerequisite":[44],"for":[45,111],"Graph":[46],"Foundation":[47],"Models":[48],"(GFMs)":[49],"in":[50],"this":[51,59],"domain":[52],"propose":[54],"four":[55,96],"design":[56],"properties":[57],"towards":[58,108],"goal.":[60],"As":[61],"proof":[63],"of":[64,81,95],"concept,":[65],"ts-net":[66],"(TopSpreadersNetwork),":[67],"solely":[69],"synthetic":[71],"multilayer":[72],"networks":[73],"(MLNs),":[74],"demonstrates":[75],"zero-shot":[76],"real-world":[79],"MLNs":[80],"varying":[82],"size":[83],"layer":[85],"count,":[86],"outperforming":[87],"classical":[88],"heuristics":[89],"baselines":[92],"three":[94],"metrics.":[97],"Based":[98],"ts-net's":[100],"performance,":[101],"we":[102],"further":[103],"outline":[104],"five":[105],"open":[106],"challenges":[107],"building":[109],"GFMs":[110],"dynamics:":[113],"scale,":[114],"many-layer":[115],"generalisation,":[116],"self-supervised":[117],"pretraining,":[118],"cross-task":[119],"transfer,":[120],"node-attribute":[122],"integration.":[123]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}