{"id":"https://openalex.org/W7164396167","doi":"https://doi.org/10.48550/arxiv.2606.11562","title":"GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs","display_name":"GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164396167","doi":"https://doi.org/10.48550/arxiv.2606.11562"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.11562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11562","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,"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.2606.11562","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138483230","display_name":"Zhuoyi Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Zhuoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138470382","display_name":"Jingzhou Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Jingzhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046115188","display_name":"Hanlin Gu","orcid":"https://orcid.org/0000-0001-8266-4561"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Hanlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138435044","display_name":"Lixin Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Lixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138395144","display_name":"Yi Yang (116183)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yi","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":0.6025000214576721,"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":0.6025000214576721,"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":0.11540000140666962,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.07320000231266022,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6718000173568726},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.635699987411499},{"id":"https://openalex.org/keywords/frontier","display_name":"Frontier","score":0.5929999947547913},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5268999934196472},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5054000020027161},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4496000111103058},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.43059998750686646}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6718000173568726},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.635699987411499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6033999919891357},{"id":"https://openalex.org/C2778571376","wikidata":"https://www.wikidata.org/wiki/Q1355821","display_name":"Frontier","level":2,"score":0.5929999947547913},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5268999934196472},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5054000020027161},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4496000111103058},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4253999888896942},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3926999866962433},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.3806999921798706},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33340001106262207},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26080000400543213},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.11562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11562","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,"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.2606.11562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11562","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,"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":[{"score":0.7786223888397217,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph":[0],"analysis":[1],"underlies":[2],"many":[3],"applications":[4],"whose":[5],"answers":[6,83],"cannot":[7,68],"be":[8],"looked":[9],"up":[10],"in":[11,114],"a":[12,18,33,42,90,99,132,159,246],"single":[13,58,116],"record":[14],"or":[15,88,222],"retrieved":[16],"along":[17,89,96],"path:":[19],"laundering":[20],"rings,":[21],"drug":[22],"repurposing,":[23],"user":[24],"preference,":[25],"and":[26,61,79,102,129,155,187],"scientific":[27],"theme":[28],"are":[29],"all":[30,81],"inferred":[31],"from":[32,85],"node":[34,59,74,87],"together":[35],"with":[36,228],"its":[37],"neighbourhood.":[38],"We":[39,136],"introduce":[40],"GraphInfer-Bench,":[41],"benchmark":[43],"for":[44],"whether":[45],"LLMs":[46,182,213],"can":[47],"perform":[48],"this":[49,70],"graph":[50,237],"inference:":[51],"producing":[52],"an":[53,240],"open-ended":[54],"answer":[55],"that":[56],"no":[57,62,115],"supports":[60],"path":[63],"retrieves.":[64],"Existing":[65],"graph-QA":[66],"protocols":[67],"test":[69],"capability:":[71],"algorithm":[72],"simulation,":[73],"classification,":[75],"single-node":[76],"description,":[77],"KG-QA,":[78],"GraphRAG":[80],"admit":[82],"retrievable":[84],"one":[86,250],"path.":[91],"GraphInfer-Bench":[92,235],"defines":[93],"five":[94],"tasks":[95,173],"Description":[97],"(what":[98],"region":[100],"is)":[101],"Comparison":[103],"(how":[104],"regions":[105],"differ),":[106],"each":[107],"constructed":[108],"so":[109],"the":[110,142,166,201,206,224,229],"ground":[111],"truth":[112],"lives":[113],"node.":[117],"The":[118],"release":[119],"contains":[120],"42,000":[121],"samples":[122],"across":[123],"six":[124],"real-world":[125],"graphs,":[126],"produced":[127],"automatically":[128],"screened":[130],"by":[131],"four-layer":[133],"quality-control":[134],"protocol.":[135],"evaluate":[137],"four":[138],"method":[139,163,204],"families":[140],"against":[141],"same":[143],"tasks:":[144],"graph-token":[145],"alignment":[146,169],"models,":[147],"zero-shot":[148],"frontier":[149,212],"closed-source":[150],"LLMs,":[151],"Graph2Text":[152,198],"supervised":[153],"fine-tuning,":[154],"plain":[156,219],"GNNs":[157,220],"as":[158,239],"structural":[160],"reference.":[161],"No":[162],"family":[164],"closes":[165],"gap.":[167],"Graph-token":[168],"partially":[170],"handles":[171],"description":[172,207],"(relational,":[174],"theme)":[175],"but":[176,193],"collapses":[177],"on":[178,184,195,205,214,232],"comparison":[179],"tasks.":[180],"Frontier":[181],"lead":[183],"outlier":[185],"detection":[186],"community":[188,233],"partition":[189],"among":[190],"LLM-based":[191,203,226],"methods":[192],"lag":[194],"masked-node":[196],"prediction.":[197],"SFT":[199],"is":[200],"strongest":[202,225],"side":[208],"yet":[209],"falls":[210],"behind":[211],"comparison.":[215],"Across":[216],"every":[217],"task,":[218],"match":[221],"beat":[223],"row,":[227],"largest":[230],"margin":[231],"detection.":[234],"surfaces":[236],"inference":[238],"open":[241],"capability":[242],"gap":[243],"rather":[244],"than":[245],"property":[247],"of":[248],"any":[249],"architecture.":[251]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-12T00:00:00"}