{"id":"https://openalex.org/W7154447653","doi":"https://doi.org/10.48550/arxiv.2604.11986","title":"Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning","display_name":"Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154447653","doi":"https://doi.org/10.48550/arxiv.2604.11986"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11986","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11986","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.2604.11986","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111436593","display_name":"Xiaoxue Han","orcid":"https://orcid.org/0000-0001-7641-7505"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Xiaoxue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133666531","display_name":"Libo Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Libo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133674586","display_name":"Zining Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Zining","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133629762","display_name":"Yue Ning","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning, Yue","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":0.784500002861023,"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":0.784500002861023,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.13330000638961792,"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":0.027000000700354576,"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/interpretability","display_name":"Interpretability","score":0.8248000144958496},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7074999809265137},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5464000105857849},{"id":"https://openalex.org/keywords/information-bottleneck-method","display_name":"Information bottleneck method","score":0.46380001306533813},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4325999915599823},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.39719998836517334}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8248000144958496},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7074999809265137},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6266000270843506},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5195000171661377},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5103999972343445},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.46380001306533813},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.39719998836517334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37070000171661377},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C64339825","wikidata":"https://www.wikidata.org/wiki/Q722659","display_name":"Graph property","level":5,"score":0.26980000734329224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11986","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11986","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.2604.11986","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11986","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":[{"display_name":"Peace, Justice and strong institutions","score":0.6492400765419006,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,102],"introduce":[1],"Graph":[2,116],"Concept":[3],"Bottleneck":[4],"(GCB)":[5],"as":[6,52],"a":[7,19,27,58],"new":[8,59],"paradigm":[9],"for":[10],"self-explainable":[11],"text-attributed":[12],"graph":[13,44],"learning.":[14],"GCB":[15,106],"maps":[16],"graphs":[17],"into":[18],"subspace,":[20],"concept":[21,25,55,66],"bottleneck,":[22],"where":[23],"each":[24],"is":[26],"meaningful":[28],"phrase,":[29],"and":[30,87,127,133],"predictions":[31],"are":[32],"made":[33],"based":[34],"on":[35,50,76,112],"the":[36,54,65,70,77,94,99],"activation":[37],"of":[38,61],"these":[39],"concepts.":[40,80],"Unlike":[41],"existing":[42],"interpretable":[43],"learning":[45],"methods":[46],"that":[47,105],"primarily":[48],"rely":[49],"subgraphs":[51],"explanations,":[53],"bottleneck":[56,72],"provides":[57],"form":[60],"interpretation.":[62],"To":[63],"refine":[64],"space,":[67],"we":[68],"apply":[69],"information":[71],"principle":[73],"to":[74,96],"focus":[75],"most":[78],"relevant":[79],"This":[81],"not":[82],"only":[83],"yields":[84],"more":[85],"concise":[86],"faithful":[88],"explanations":[89],"but":[90],"also":[91],"explicitly":[92],"guides":[93],"model":[95],"\"think\"":[97],"toward":[98],"correct":[100],"decision.":[101],"empirically":[103],"show":[104],"achieves":[107],"intrinsic":[108],"interpretability":[109],"with":[110,114],"accuracy":[111],"par":[113],"black-box":[115],"Neural":[117],"Networks.":[118],"Moreover,":[119],"it":[120],"delivers":[121],"better":[122],"performance":[123],"under":[124],"distribution":[125],"shifts":[126],"data":[128],"perturbations,":[129],"showing":[130],"improved":[131],"robustness":[132],"generalizability,":[134],"benefitting":[135],"from":[136],"concept-guided":[137],"prediction.":[138]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}