{"id":"https://openalex.org/W7114766632","doi":"https://doi.org/10.48550/arxiv.2512.07878","title":"Graph Contrastive Learning via Spectral Graph Alignment","display_name":"Graph Contrastive Learning via Spectral Graph Alignment","publication_year":2025,"publication_date":"2025-11-27","ids":{"openalex":"https://openalex.org/W7114766632","doi":"https://doi.org/10.48550/arxiv.2512.07878"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.07878","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.07878","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.2512.07878","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Nguyen, Manh","orcid":null},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nguyen, Manh","raw_affiliation_strings":["Department of Statistics, University of Wisconsin-Madison"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I135310074"],"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":0.951200008392334,"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":0.951200008392334,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.01119999960064888,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.004999999888241291,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.6783000230789185},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5616999864578247},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.5040000081062317},{"id":"https://openalex.org/keywords/ideal","display_name":"Ideal (ethics)","score":0.3880000114440918},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33480000495910645}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6783000230789185},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5616999864578247},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5156999826431274},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4609000086784363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4375},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.3880000114440918},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3822000026702881},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.07878","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.07878","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.2512.07878","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.07878","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Given":[0],"augmented":[1],"views":[2,19],"of":[3,15,29,102],"each":[4],"input":[5],"graph,":[6],"contrastive":[7,85],"learning":[8,111,114,125],"methods":[9],"(e.g.,":[10],"InfoNCE)":[11],"optimize":[12],"pairwise":[13],"alignment":[14],"graph":[16],"embeddings":[17],"across":[18],"while":[20],"providing":[21],"no":[22],"mechanism":[23],"to":[24],"control":[25],"the":[26,30,47,53,66,78,81,88,94,103],"global":[27],"structure":[28],"view":[31,48],"specific":[32,49],"graph-of-graphs":[33,50],"built":[34],"from":[35],"these":[36],"embeddings.":[37],"We":[38],"introduce":[39],"SpecMatch-CL,":[40],"a":[41],"novel":[42],"loss":[43,86],"function":[44],"that":[45,62],"aligns":[46],"by":[51],"minimizing":[52],"difference":[54,67,79],"between":[55,68,80],"their":[56],"normalized":[57,69],"Laplacians.":[58],"Theoretically,":[59],"we":[60],"show":[61],"under":[63,109],"certain":[64],"assumptions,":[65],"Laplacians":[70],"provides":[71],"an":[72],"upper":[73],"bound":[74],"not":[75],"only":[76],"for":[77,93],"ideal":[82],"Perfect":[83],"Alignment":[84],"and":[87,112,119,128],"current":[89],"loss,":[90],"but":[91],"also":[92],"Uniformly":[95],"loss.":[96],"Empirically,":[97],"SpecMatch-CL":[98],"establishes":[99],"new":[100],"state":[101],"art":[104],"on":[105,126],"eight":[106],"TU":[107],"benchmarks":[108],"unsupervised":[110],"semi-supervised":[113],"at":[115],"low":[116],"label":[117],"rates,":[118],"yields":[120],"consistent":[121],"gains":[122],"in":[123],"transfer":[124],"PPI-306K":[127],"ZINC":[129],"2M":[130],"datasets.":[131]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-12-11T00:00:00"}