{"id":"https://openalex.org/W2161902954","doi":"https://doi.org/10.1145/1553374.1553447","title":"Learning spectral graph transformations for link prediction","display_name":"Learning spectral graph transformations for link prediction","publication_year":2009,"publication_date":"2009-06-14","ids":{"openalex":"https://openalex.org/W2161902954","doi":"https://doi.org/10.1145/1553374.1553447","mag":"2161902954"},"language":"en","primary_location":{"id":"doi:10.1145/1553374.1553447","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1553374.1553447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th Annual International Conference on Machine Learning","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016952117","display_name":"J\u00e9r\u00f4me Kunegis","orcid":null},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"J\u00e9r\u00f4me Kunegis","raw_affiliation_strings":["Technische Universit\u00e4t Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit\u00e4t Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105405650","display_name":"Andreas Lommatzsch","orcid":"https://orcid.org/0009-0009-0532-7081"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Lommatzsch","raw_affiliation_strings":["Technische Universit\u00e4t Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit\u00e4t Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5016952117"],"corresponding_institution_ids":["https://openalex.org/I4577782"],"apc_list":null,"apc_paid":null,"fwci":10.1319,"has_fulltext":false,"cited_by_count":189,"citation_normalized_percentile":{"value":0.98848956,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"561","last_page":"568"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.98089998960495,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6986383199691772},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.5875116586685181},{"id":"https://openalex.org/keywords/hyperlink","display_name":"Hyperlink","score":0.5820368528366089},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.568610668182373},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5067632794380188},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.46564802527427673},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.46505674719810486},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4342300295829773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3475978970527649},{"id":"https://openalex.org/keywords/web-page","display_name":"Web page","score":0.1132434606552124}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6986383199691772},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.5875116586685181},{"id":"https://openalex.org/C30088001","wikidata":"https://www.wikidata.org/wiki/Q102014","display_name":"Hyperlink","level":3,"score":0.5820368528366089},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.568610668182373},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5067632794380188},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.46564802527427673},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.46505674719810486},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4342300295829773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3475978970527649},{"id":"https://openalex.org/C21959979","wikidata":"https://www.wikidata.org/wiki/Q36774","display_name":"Web page","level":2,"score":0.1132434606552124},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1553374.1553447","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1553374.1553447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th Annual International Conference on Machine Learning","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.149.6681","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.6681","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.mcgill.ca/~icml2009/papers/281.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W14071851","https://openalex.org/W1511160855","https://openalex.org/W1575340908","https://openalex.org/W1774304772","https://openalex.org/W1832221731","https://openalex.org/W2001692463","https://openalex.org/W2054079656","https://openalex.org/W2086605603","https://openalex.org/W2104535569","https://openalex.org/W2104876468","https://openalex.org/W2117354486","https://openalex.org/W2118131693","https://openalex.org/W2125315567","https://openalex.org/W2128269848","https://openalex.org/W2142845314","https://openalex.org/W2144780381","https://openalex.org/W2155599429","https://openalex.org/W2161984370","https://openalex.org/W2420733993","https://openalex.org/W2769133055","https://openalex.org/W2963442158","https://openalex.org/W4298299405","https://openalex.org/W6672105629","https://openalex.org/W6681124392"],"related_works":["https://openalex.org/W2583864867","https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241"],"abstract_inverted_index":{"We":[0,45,81,94],"present":[1],"a":[2,22,38,62],"unified":[3],"framework":[4],"for":[5],"learning":[6],"link":[7],"prediction":[8,12,52],"and":[9,32,36,75,91,112],"edge":[10],"weight":[11],"functions":[13,53],"in":[14],"large":[15],"networks,":[16,103,107,109],"based":[17],"on":[18,70],"the":[19,48,59,71],"transformation":[20],"of":[21,50],"graph's":[23],"algebraic":[24],"spectrum.":[25],"Our":[26],"approach":[27],"generalizes":[28],"several":[29],"graph":[30],"kernels":[31],"dimensionality":[33],"reduction":[34],"methods":[35],"provides":[37],"method":[39,97],"to":[40,61,86],"estimate":[41],"their":[42],"parameters":[43,49],"efficiently.":[44],"show":[46],"how":[47],"these":[51],"can":[54,77],"be":[55,78],"learned":[56],"by":[57],"reducing":[58],"problem":[60,65],"one-dimensional":[63],"regression":[64],"whose":[66],"runtime":[67],"only":[68],"depends":[69],"method's":[72],"reduced":[73],"rank":[74],"that":[76,84],"inspected":[79],"visually.":[80],"derive":[82],"variants":[83],"apply":[85],"undirected,":[87],"weighted,":[88],"unweighted,":[89],"unipartite":[90],"bipartite":[92],"graphs.":[93],"evaluate":[95],"our":[96],"experimentally":[98],"using":[99],"examples":[100],"from":[101],"social":[102],"collaborative":[104],"filtering,":[105],"trust":[106],"citation":[108],"authorship":[110],"graphs":[111],"hyperlink":[113],"networks.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":11},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":15},{"year":2016,"cited_by_count":11},{"year":2015,"cited_by_count":18},{"year":2014,"cited_by_count":15},{"year":2013,"cited_by_count":16},{"year":2012,"cited_by_count":19}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}