{"id":"https://openalex.org/W4417088513","doi":"https://doi.org/10.48550/arxiv.2511.16778","title":"GCL-OT: Graph Contrastive Learning with Optimal Transport for Heterophilic Text-Attributed Graphs","display_name":"GCL-OT: Graph Contrastive Learning with Optimal Transport for Heterophilic Text-Attributed Graphs","publication_year":2025,"publication_date":"2025-11-20","ids":{"openalex":"https://openalex.org/W4417088513","doi":"https://doi.org/10.48550/arxiv.2511.16778"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.16778","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.16778","pdf_url":"https://arxiv.org/pdf/2511.16778","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.16778","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043804622","display_name":"Yating Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ren, Yating","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047387636","display_name":"Yikun Ban","orcid":"https://orcid.org/0000-0003-3035-4849"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ban, Yikun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5087082349","display_name":"Huobin Tan","orcid":"https://orcid.org/0000-0003-3113-6552"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Huobin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043804622"],"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":0.9882000088691711,"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":0.9882000088691711,"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":0.0035000001080334187,"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":0.002400000113993883,"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/graph","display_name":"Graph","score":0.5396000146865845},{"id":"https://openalex.org/keywords/homophily","display_name":"Homophily","score":0.49720001220703125},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4510999917984009},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.421099990606308},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.41990000009536743},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.39489999413490295},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3352000117301941},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3246999979019165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7127000093460083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5647000074386597},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5396000146865845},{"id":"https://openalex.org/C2779812341","wikidata":"https://www.wikidata.org/wiki/Q5891525","display_name":"Homophily","level":2,"score":0.49720001220703125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4519999921321869},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.421099990606308},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.39489999413490295},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38019999861717224},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26930001378059387},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2687000036239624},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2581999897956848}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2511.16778","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.16778","pdf_url":"https://arxiv.org/pdf/2511.16778","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"pmh:doi:10.48550/arxiv.2511.16778","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2511.16778","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.16778","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.16778","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.16778","pdf_url":"https://arxiv.org/pdf/2511.16778","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"structure-text":[1,89],"contrastive":[2,113],"learning":[3,114,179],"has":[4],"shown":[5],"promising":[6],"performance":[7],"on":[8,28,200],"text-attributed":[9,77],"graphs":[10],"by":[11],"leveraging":[12],"the":[13,178,190],"complementary":[14],"strengths":[15],"of":[16,126,180],"graph":[17,112],"neural":[18],"networks":[19],"and":[20,34,84,97,104,194,212],"language":[21],"models.":[22],"However,":[23],"existing":[24,46],"methods":[25,47],"typically":[26],"rely":[27],"homophily":[29],"assumptions":[30],"in":[31,76],"similarity":[32,136],"estimation":[33],"hard":[35],"optimization":[36],"objectives,":[37],"which":[38,87],"limit":[39],"their":[40],"applicability":[41],"to":[42,66,94,138,170],"heterophilic":[43],"graphs.":[44],"Although":[45],"can":[48,188],"mitigate":[49],"heterophily":[50,75],"through":[51],"structural":[52],"adjustments":[53],"or":[54],"neighbor":[55],"aggregation,":[56],"they":[57],"usually":[58],"treat":[59],"textual":[60],"embeddings":[61],"as":[62],"static":[63],"targets,":[64],"leading":[65],"suboptimal":[67],"alignment.":[68,163],"In":[69],"this":[70],"work,":[71],"we":[72,107,132,150,165],"identify":[73],"multi-granular":[74],"graphs,":[78],"including":[79],"complete":[80,148],"heterophily,":[81,83,131,149],"partial":[82,130],"latent":[85,181],"homophily,":[86],"makes":[88],"alignment":[90],"particularly":[91],"challenging":[92],"due":[93],"mixed,":[95],"noisy,":[96],"missing":[98],"semantic":[99],"correlations.":[100],"To":[101],"achieve":[102],"flexible":[103],"bidirectional":[105],"alignment,":[106],"propose":[108],"GCL-OT,":[109],"a":[110,134,152],"novel":[111],"framework":[115],"with":[116,120,174],"optimal":[117,161],"transport,":[118],"equipped":[119],"tailored":[121],"mechanisms":[122],"for":[123,129],"each":[124],"type":[125],"heterophily.":[127],"Specifically,":[128],"design":[133],"RealSoftMax-based":[135],"estimator":[137],"emphasize":[139],"key":[140],"neighbor-word":[141],"interactions":[142],"while":[143],"easing":[144],"background":[145],"noise.":[146],"For":[147],"introduce":[151],"prompt-based":[153],"filter":[154],"that":[155,186,204],"adaptively":[156],"excludes":[157],"irrelevant":[158],"noise":[159],"during":[160],"transport":[162],"Furthermore,":[164],"incorporate":[166],"OT-guided":[167],"soft":[168],"supervision":[169],"uncover":[171],"potential":[172],"neighbors":[173],"similar":[175],"semantics,":[176],"enhancing":[177],"homophily.":[182],"Theoretical":[183],"analysis":[184],"shows":[185],"GCL-OT":[187,205],"improve":[189],"mutual":[191],"information":[192],"bound":[193],"Bayes":[195],"error":[196],"guarantees.":[197],"Extensive":[198],"experiments":[199],"nine":[201],"benchmarks":[202],"show":[203],"outperforms":[206],"state-of-the-art":[207],"methods,":[208],"demonstrating":[209],"its":[210],"effectiveness":[211],"robustness.":[213]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-11-25T00:00:00"}