{"id":"https://openalex.org/W7204482802","doi":"https://doi.org/10.48550/arxiv.2608.25048","title":"Tabular Foundation Models for Multi-View Information Cascade Popularity Prediction","display_name":"Tabular Foundation Models for Multi-View Information Cascade Popularity Prediction","publication_year":2026,"publication_date":"2026-08-25","ids":{"openalex":"https://openalex.org/W7204482802","doi":"https://doi.org/10.48550/arxiv.2608.25048"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.25048","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.25048","pdf_url":"https://arxiv.org/pdf/2608.25048","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.25048","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148431014","display_name":"Wenting Zhu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhu, Wenting","raw_affiliation_strings":["Beijing University of Posts and Telecommunications Beijing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications Beijing , China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113356134","display_name":"Chenghua Gong","orcid":"https://orcid.org/0009-0003-1988-6702","observed_orcids":["https://orcid.org/0009-0003-1988-6702"]},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gong, Chenghua","raw_affiliation_strings":["University of Science and Technology of China Hefei , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China Hefei , China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002277969","display_name":"Sanchuan Guo","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guo, Sanchuan","raw_affiliation_strings":["Beijing University of Posts and Telecommunications Beijing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications Beijing , China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148431142","display_name":"Chaozhuo Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li, Chaozhuo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications Beijing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications Beijing , China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113880901","display_name":"Yueyue Zhang","orcid":"https://orcid.org/0009-0002-8047-9412","observed_orcids":["https://orcid.org/0009-0002-8047-9412","https://orcid.org/0000-0001-7271-1555"]},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang, Yueyue","raw_affiliation_strings":["Beijing University of Posts and Telecommunications Beijing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications Beijing , China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5148436998","display_name":"Xi Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang, Xi","raw_affiliation_strings":["Beijing University of Posts and Telecommunications Beijing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications Beijing , China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.5565999746322632,"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"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.5565999746322632,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.15889999270439148,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11147","display_name":"Misinformation and Its Impacts","score":0.028699999675154686,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tabular-foundation-models","display_name":"tabular foundation models","score":0.9660000205039978},{"id":"https://openalex.org/keywords/social-media-engagement","display_name":"social media engagement","score":0.6520000100135803},{"id":"https://openalex.org/keywords/in-context-learning","display_name":"in-context learning","score":0.7519999742507935},{"id":"https://openalex.org/keywords/neural-odes","display_name":"neural ordinary differential equations","score":0.5619999766349792},{"id":"https://openalex.org/keywords/cross-attention-fusion","display_name":"cross-attention fusion","score":0.7419999837875366},{"id":"https://openalex.org/keywords/parameter-efficient-fine-tuning","display_name":"parameter-efficient fine-tuning","score":0.8330000042915344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7857999801635742},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.7558000087738037},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.7365999817848206},{"id":"https://openalex.org/C27286358","wikidata":"https://www.wikidata.org/wiki/Q6031027","display_name":"Information cascade","level":2,"score":0.6373999714851379},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.554099977016449},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.534600019454956},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49799999594688416},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.48840001225471497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46860000491142273},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4609000086784363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.450300008058548},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4401000142097473},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3666999936103821},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2655999958515167},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2615000009536743},{"id":"https://openalex.org/C180198813","wikidata":"https://www.wikidata.org/wiki/Q121182","display_name":"Information system","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.25048","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.25048","pdf_url":"https://arxiv.org/pdf/2608.25048","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.25048","doi":"https://doi.org/10.48550/arxiv.2608.25048","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.25048","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.25048","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.25048","pdf_url":"https://arxiv.org/pdf/2608.25048","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204482802.pdf","grobid_xml":"https://content.openalex.org/works/W7204482802.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W4226412614","https://openalex.org/W4302009014","https://openalex.org/W4406123282","https://openalex.org/W4415187890","https://openalex.org/W4417414862","https://openalex.org/W7161262295"],"related_works":["https://openalex.org/W4320352328","https://openalex.org/W4409365446","https://openalex.org/W4405974745","https://openalex.org/W4411131652","https://openalex.org/W4402352254","https://openalex.org/W4225530754","https://openalex.org/W7213239581","https://openalex.org/W4406094151","https://openalex.org/W4399450801","https://openalex.org/W7168359432"],"abstract_inverted_index":{"Predicting":[0],"the":[1,29,65,84,97,104,119,124,128,146,152],"future":[2],"popularity":[3,75],"of":[4,86],"information":[5,11,35,89,190],"cascades":[6],"is":[7],"essential":[8],"for":[9,145],"understanding":[10],"diffusion":[12],"on":[13,28],"social":[14],"media.":[15],"Despite":[16],"recent":[17],"advances,":[18],"existing":[19],"methods":[20],"face":[21],"two":[22,138],"key":[23],"limitations:":[24],"they":[25,51],"focus":[26],"primarily":[27],"cascade":[30,121,130,156,184],"view":[31],"while":[32,123,172],"overlooking":[33],"other":[34],"views":[36,113],"that":[37,107,186,195],"drive":[38],"user":[39],"engagement,":[40],"such":[41],"as":[42,103],"textual":[43],"semantics,":[44],"visual":[45],"content,":[46],"and":[47,50,204],"tabular":[48,70,80],"attributes;":[49],"fail":[52],"to":[53,68,82,117,150,154],"capture":[54],"high-order":[55],"cross-view":[56],"interactions.":[57],"To":[58],"address":[59],"these":[60],"issues,":[61],"we":[62,158,179],"propose":[63],"\\textbf{TFM4POP},":[64],"first":[66],"framework":[67],"introduce":[69],"foundation":[71],"models":[72],"(TFMs)":[73],"into":[74],"prediction,":[76],"leveraging":[77],"their":[78],"pre-trained":[79],"priors":[81],"unify":[83],"modeling":[85],"multiple":[87,202],"heterogeneous":[88],"views.":[90,191],"Specifically,":[91],"TFM4POP":[92,196],"adopts":[93],"a":[94,101,133,181],"dual-branch":[95],"design:":[96],"static":[98,112,120],"branch":[99,126],"employs":[100],"TFM":[102,153],"feature-encoding":[105],"backbone":[106],"jointly":[108],"reasons":[109],"over":[110],"all":[111,188],"through":[114],"in-context":[115],"learning":[116],"produce":[118],"representation,":[122],"dynamic":[125],"captures":[127],"continuous-time":[129],"dynamics":[131],"with":[132,166],"dedicated":[134],"Neural-ODE-based":[135],"encoder.":[136],"The":[137],"representations":[139],"are":[140],"then":[141],"fused":[142],"via":[143],"cross-attention":[144],"final":[147],"prediction.":[148],"Furthermore,":[149],"adapt":[151],"real":[155],"distributions,":[157],"apply":[159],"parameter-efficient":[160],"IA3":[161],"fine-tuning,":[162],"achieving":[163],"performance":[164],"competitive":[165],"or":[167],"better":[168],"than":[169],"full":[170],"fine-tuning":[171],"updating":[173],"substantially":[174],"fewer":[175],"parameters.":[176],"In":[177],"addition,":[178],"construct":[180],"comprehensive":[182],"multi-view":[183],"benchmark":[185],"covers":[187],"four":[189],"Extensive":[192],"experiments":[193],"show":[194],"consistently":[197],"outperforms":[198],"state-of-the-art":[199],"baselines":[200],"across":[201],"datasets":[203],"observation":[205],"settings.":[206]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-28T00:00:00"}