{"id":"https://openalex.org/W7162549249","doi":"https://doi.org/10.48550/arxiv.2605.27176","title":"The Compressive Knowledge Graph Hypothesis: Which Graph Facts Matter for Scientific Hypothesis Generation?","display_name":"The Compressive Knowledge Graph Hypothesis: Which Graph Facts Matter for Scientific Hypothesis Generation?","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162549249","doi":"https://doi.org/10.48550/arxiv.2605.27176"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27176","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.2605.27176","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137127775","display_name":"Shashwat Sourav","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sourav, Shashwat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019337682","display_name":"Viktoriia Baibakova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baibakova, Viktoriia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137172423","display_name":"Sanjay Das","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Sanjay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093766594","display_name":"Ran Elgedawy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elgedawy, Ran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063640628","display_name":"Maria Mahbub","orcid":"https://orcid.org/0000-0002-3422-9650"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahbub, Maria","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136774216","display_name":"Emily Herron","orcid":"https://orcid.org/0000-0002-7300-8172"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Herron, Emily","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137139677","display_name":"Tirthankar Ghosal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ghosal, Tirthankar","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":0.972000002861023,"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":0.972000002861023,"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/T10028","display_name":"Topic Modeling","score":0.008500000461935997,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.00559999980032444,"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/knowledge-graph","display_name":"Knowledge graph","score":0.6079000234603882},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5648000240325928},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4325000047683716},{"id":"https://openalex.org/keywords/random-geometric-graph","display_name":"Random geometric graph","score":0.3961000144481659},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.364300012588501},{"id":"https://openalex.org/keywords/ontology","display_name":"Ontology","score":0.3499999940395355},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.3467000126838684}],"concepts":[{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.6079000234603882},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5648000240325928},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5467000007629395},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5267000198364258},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C30609935","wikidata":"https://www.wikidata.org/wiki/Q7291969","display_name":"Random geometric graph","level":5,"score":0.3961000144481659},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.364300012588501},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C13251829","wikidata":"https://www.wikidata.org/wiki/Q3085841","display_name":"Dense graph","level":5,"score":0.3260999917984009},{"id":"https://openalex.org/C64339825","wikidata":"https://www.wikidata.org/wiki/Q722659","display_name":"Graph property","level":5,"score":0.3215999901294708},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2851000130176544},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28459998965263367},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27869999408721924},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C112953755","wikidata":"https://www.wikidata.org/wiki/Q739462","display_name":"Graph drawing","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2669999897480011},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2599000036716461},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2549999952316284},{"id":"https://openalex.org/C68103157","wikidata":"https://www.wikidata.org/wiki/Q569347","display_name":"Graph product","level":5,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27176","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.2605.27176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27176","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Knowledge":[0],"graphs":[1],"(KGs)":[2],"can":[3,114],"provide":[4],"structured":[5,138],"scientific":[6],"context":[7,69],"to":[8,105],"language":[9],"models,":[10,61],"but":[11,72],"it":[12],"remains":[13],"unclear":[14],"which":[15],"graph":[16,68,78],"facts":[17],"actually":[18],"shape":[19],"the":[20,98,119,143],"generated":[21],"hypotheses.":[22],"We":[23,38],"study":[24],"KG-guided":[25],"hypothesis":[26],"generation":[27],"for":[28],"battery":[29],"materials":[30],"across":[31],"Mistral-7B,":[32],"Llama-3.1-70B,":[33],"and":[34,48,51,57,66,111],"Gemini":[35],"2.5":[36],"Flash.":[37],"perturb":[39],"local":[40,145],"KGs":[41],"by":[42],"varying":[43],"density,":[44],"ontology":[45],"richness,":[46],"topology,":[47],"control":[49],"structure,":[50],"evaluate":[52],"outputs":[53,74],"with":[54],"both":[55],"provided-graph":[56],"fixed-reference":[58],"metrics.":[59],"Across":[60],"KG":[62,127,130],"utility":[63],"is":[64,102,132],"selective":[65],"model-dependent:":[67],"changes":[70],"outputs,":[71],"no-KG":[73],"also":[75,115],"recover":[76,116],"substantial":[77],"content":[79],"from":[80,135],"model":[81],"priors.":[82],"Compact":[83],"top-k":[84],"subgraphs":[85,139],"often":[86,133],"approximate":[87],"full-KG":[88],"behavior,":[89],"including":[90],"when":[91],"claimed-outcome":[92],"triples":[93],"are":[94],"held":[95],"out.":[96],"At":[97],"same":[99],"time,":[100],"compression":[101],"not":[103],"unique":[104],"one":[106],"semantic":[107],"ranking":[108],"rule,":[109],"random":[110],"topology-based":[112],"subsets":[113],"much":[117],"of":[118],"signal.":[120],"These":[121],"results":[122],"support":[123],"a":[124],"redundancy-aware":[125],"Compressive":[126],"hypothesis:":[128],"useful":[129],"signal":[131],"recoverable":[134],"compact,":[136],"scientifically":[137],"rather":[140],"than":[141],"requiring":[142],"full":[144],"graph.":[146]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-28T00:00:00"}
