{"id":"https://openalex.org/W3133986297","doi":"https://doi.org/10.23919/eusipco54536.2021.9615920","title":"Learning Multi-layer Graphs and a Common Representation for Clustering","display_name":"Learning Multi-layer Graphs and a Common Representation for Clustering","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3133986297","doi":"https://doi.org/10.23919/eusipco54536.2021.9615920","mag":"3133986297"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco54536.2021.9615920","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9615920","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","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/A5076283669","display_name":"Sravanthi Gurugubelli","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sravanthi Gurugubelli","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015585417","display_name":"Sundeep Prabhakar Chepuri","orcid":"https://orcid.org/0000-0002-0429-6588"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sundeep Prabhakar Chepuri","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076283669"],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":0.6829,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60107335,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1830","last_page":"1834"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9980999827384949,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.988099992275238,"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/computer-science","display_name":"Computer science","score":0.6471327543258667},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6441549062728882},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4867360591888428},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.47717753052711487},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45220234990119934},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.41644084453582764},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33596140146255493},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2768276035785675}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6471327543258667},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6441549062728882},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4867360591888428},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.47717753052711487},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45220234990119934},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.41644084453582764},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33596140146255493},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2768276035785675}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco54536.2021.9615920","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9615920","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W191001584","https://openalex.org/W287609105","https://openalex.org/W602904462","https://openalex.org/W2050680319","https://openalex.org/W2059861509","https://openalex.org/W2080161383","https://openalex.org/W2132914434","https://openalex.org/W2252433728","https://openalex.org/W2462991260","https://openalex.org/W2752527720","https://openalex.org/W2808465901","https://openalex.org/W2886098248","https://openalex.org/W2891436983","https://openalex.org/W2902091715","https://openalex.org/W2963384510","https://openalex.org/W2963460103","https://openalex.org/W2964012239","https://openalex.org/W2964171990","https://openalex.org/W2994097903","https://openalex.org/W3016246029","https://openalex.org/W3099302384","https://openalex.org/W3100282875","https://openalex.org/W3100757735","https://openalex.org/W3203218609","https://openalex.org/W4293651439"],"related_works":["https://openalex.org/W1482912984","https://openalex.org/W2902850141","https://openalex.org/W2607797544","https://openalex.org/W2885900820","https://openalex.org/W1556852487","https://openalex.org/W2136935487","https://openalex.org/W2265158691","https://openalex.org/W1955548541","https://openalex.org/W2032317191","https://openalex.org/W2613055102"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,57],"focus":[4],"on":[5,116,130],"graph":[6,30,61,67,83,89,125],"learning":[7,126],"from":[8],"multi-view":[9,25,142],"data":[10,26,55],"of":[11,43,49,64,72,81],"shared":[12,39],"entities":[13,23],"for":[14,91],"spectral":[15],"clustering.":[16,92],"We":[17,77,110],"can":[18],"explain":[19],"interactions":[20],"between":[21],"the":[22,38,47,50,60,65,73,79,82,100,103,108,121,137],"in":[24,107],"using":[27],"a":[28,32,53],"multi-layer":[29,123],"with":[31],"common":[33,74,97],"vertex":[34,75],"set,":[35],"which":[36],"represents":[37],"entities.":[40,51],"The":[41,93],"edges":[42],"different":[44],"layers":[45,68,90],"capture":[46],"relationships":[48],"Assuming":[52],"smoothness":[54],"model,":[56],"jointly":[58],"estimate":[59],"Laplacian":[62,84],"matrices":[63,85],"individual":[66],"and":[69,132],"low-dimensional":[70,94],"embedding":[71],"set.":[76],"constrain":[78],"rank":[80],"to":[86,98,119],"obtain":[87],"multi-component":[88,124],"node":[95],"embeddings,":[96],"all":[99],"views,":[101],"assimilate":[102],"complementary":[104],"information":[105],"present":[106],"views.":[109],"propose":[111],"an":[112],"efficient":[113],"solver":[114],"based":[115],"alternating":[117],"minimization":[118],"solve":[120],"proposed":[122,138],"problem.":[127],"Numerical":[128],"experiments":[129],"synthetic":[131],"real":[133],"datasets":[134],"demonstrate":[135],"that":[136],"algorithm":[139],"outperforms":[140],"state-of-the-art":[141],"clustering":[143],"techniques.":[144]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}