{"id":"https://openalex.org/W7164029405","doi":"https://doi.org/10.48550/arxiv.2606.09301","title":"PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning","display_name":"PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164029405","doi":"https://doi.org/10.48550/arxiv.2606.09301"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09301","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.2606.09301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138202744","display_name":"Zekai Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zekai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138214978","display_name":"Miao Zhang (17216)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Miao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138221559","display_name":"Jiayang Xing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing, Jiayang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138275885","display_name":"Xunkai Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xunkai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138221797","display_name":"Xun Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Xun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138272970","display_name":"Rong-Hua Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Rong-Hua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138253301","display_name":"Guoren Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Guoren","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":0.9616000056266785,"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":0.9616000056266785,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.009100000374019146,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.005400000140070915,"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/knowledge-graph","display_name":"Knowledge graph","score":0.6029999852180481},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5152999758720398},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5095999836921692},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3467000126838684},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.3131999969482422},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.3100999891757965}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.78329998254776},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.6029999852180481},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5152999758720398},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5095999836921692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4706000089645386},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3986999988555908},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3239000141620636},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.30649998784065247},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2937000095844269},{"id":"https://openalex.org/C32900221","wikidata":"https://www.wikidata.org/wiki/Q181365","display_name":"Dot product","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2745000123977661},{"id":"https://openalex.org/C68103157","wikidata":"https://www.wikidata.org/wiki/Q569347","display_name":"Graph product","level":5,"score":0.26649999618530273},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09301","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.2606.09301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09301","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"federated":[1,115],"graph":[2,79,141,150],"learning":[3],"(MM-FGL)":[4],"aims":[5],"to":[6,50,71],"collaboratively":[7],"learn":[8],"from":[9,126,133],"decentralized":[10],"graphs":[11,32],"with":[12],"text":[13,43],"and":[14,93,108,136,154],"images.":[15,47],"However,":[16],"real-world":[17],"clients":[18],"may":[19,29,41],"not":[20],"share":[21],"a":[22,26,38,62,113],"common":[23],"modality":[24,56,124],"basis:":[25],"visual-search":[27],"client":[28,40,64],"contain":[30],"image--interaction":[31],"but":[33,44],"no":[34,45],"seller":[35],"descriptions,":[36],"while":[37],"catalog":[39],"provide":[42],"product":[46],"We":[48],"refer":[49],"this":[51,101],"practical":[52],"setting":[53],"as":[54],"client-level":[55],"deficiency.":[57],"Unlike":[58],"random":[59],"instance-wise":[60],"missingness,":[61],"deficient":[63],"lacks":[65],"the":[66,73,96,122,134],"local":[67,127,140],"semantic":[68],"basis":[69],"needed":[70],"reconstruct":[72],"absent":[74],"modality.":[75],"More":[76],"importantly,":[77],"in":[78],"learning,":[80],"incomplete":[81],"representations":[82],"initialize":[83],"message":[84],"passing,":[85],"so":[86],"imputation":[87,117],"errors":[88],"can":[89],"be":[90],"filtered,":[91],"mixed,":[92],"amplified":[94],"by":[95,167],"receiving":[97],"topology.":[98],"To":[99],"address":[100],"gap,":[102],"we":[103],"propose":[104],"\\textbf{PRISM}":[105],"(\\textbf{P}roactive":[106],"\\textbf{R}etrieval":[107],"\\textbf{I}mputation":[109],"via":[110],"\\textbf{S}tructural":[111],"\\textbf{M}eta-prompting),":[112],"topology-aware":[114,144],"cross-modal":[116],"framework.":[118],"Rather":[119],"than":[120],"reconstructing":[121],"missing":[123],"solely":[125],"observations,":[128],"PRISM":[129,159],"recovers":[130],"missing-modality":[131],"semantics":[132],"federation":[135],"introduces":[137],"them":[138],"into":[139],"propagation":[142],"under":[143],"control.":[145],"Experiments":[146],"on":[147,169],"six":[148],"multimodal":[149],"datasets":[151],"across":[152],"graph-centric":[153],"modality-centric":[155],"tasks":[156],"show":[157],"that":[158],"consistently":[160],"improves":[161],"modality-deficient":[162],"clients,":[163],"outperforming":[164],"state-of-the-art":[165],"baselines":[166],"\\textbf{4.48}\\%":[168],"average.":[170]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}
