{"id":"https://openalex.org/W2912438878","doi":"https://doi.org/10.1109/acssc.2018.8645150","title":"Graph Heat Mixture Model Learning","display_name":"Graph Heat Mixture Model Learning","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2912438878","doi":"https://doi.org/10.1109/acssc.2018.8645150","mag":"2912438878"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2018.8645150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645150","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","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":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1901.08585","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065772016","display_name":"Hermina Petric Mareti\u0107","orcid":"https://orcid.org/0000-0001-7780-9244"},"institutions":[{"id":"https://openalex.org/I4210121626","display_name":"Signal Processing (United States)","ror":"https://ror.org/021gzyw51","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121626"]},{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Hermina Petric Maretic","raw_affiliation_strings":["Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Signal Processing Laboratory (LTS4)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Signal Processing Laboratory (LTS4)","institution_ids":["https://openalex.org/I4210121626","https://openalex.org/I5124864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088281690","display_name":"Mireille El Gheche","orcid":"https://orcid.org/0000-0002-1035-9768"},"institutions":[{"id":"https://openalex.org/I4210121626","display_name":"Signal Processing (United States)","ror":"https://ror.org/021gzyw51","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121626"]},{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Mireille El Gheche","raw_affiliation_strings":["Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Signal Processing Laboratory (LTS4)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Signal Processing Laboratory (LTS4)","institution_ids":["https://openalex.org/I4210121626","https://openalex.org/I5124864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000947076","display_name":"Pascal Frossard","orcid":"https://orcid.org/0000-0002-4010-714X"},"institutions":[{"id":"https://openalex.org/I4210121626","display_name":"Signal Processing (United States)","ror":"https://ror.org/021gzyw51","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121626"]},{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Pascal Frossard","raw_affiliation_strings":["Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Signal Processing Laboratory (LTS4)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Signal Processing Laboratory (LTS4)","institution_ids":["https://openalex.org/I4210121626","https://openalex.org/I5124864"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":"1003","last_page":"1007"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9890999794006348,"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/T10581","display_name":"Neural dynamics and brain function","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6788544654846191},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6540191173553467},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6028625965118408},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5412063598632812},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5342599153518677},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5018243789672852},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4858984351158142},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.45039787888526917},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41236746311187744},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35573291778564453},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34282395243644714},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3394186794757843}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6788544654846191},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6540191173553467},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6028625965118408},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5412063598632812},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5342599153518677},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5018243789672852},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4858984351158142},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.45039787888526917},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41236746311187744},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35573291778564453},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34282395243644714},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3394186794757843},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/acssc.2018.8645150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645150","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","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":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1901.08585","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1901.08585","pdf_url":"https://arxiv.org/pdf/1901.08585","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2912438878","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1901.08585","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:infoscience.epfl.ch:266416","is_oa":true,"landing_page_url":"https://infoscience.epfl.ch/handle/20.500.14299/156968","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference proceedings"},{"id":"doi:10.48550/arxiv.1901.08585","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1901.08585","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":"pmh:oai:arXiv.org:1901.08585","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1901.08585","pdf_url":"https://arxiv.org/pdf/1901.08585","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2100556411","https://openalex.org/W2137096596","https://openalex.org/W2171878761","https://openalex.org/W2585019672","https://openalex.org/W2615556757","https://openalex.org/W2798729876","https://openalex.org/W2885442657","https://openalex.org/W2959406683","https://openalex.org/W2962685284","https://openalex.org/W2962752727","https://openalex.org/W2962759781","https://openalex.org/W2962886701","https://openalex.org/W2963384510","https://openalex.org/W2964012239","https://openalex.org/W3014498372","https://openalex.org/W4238253035","https://openalex.org/W6689213722","https://openalex.org/W6749876080","https://openalex.org/W6755861360","https://openalex.org/W6766505652"],"related_works":["https://openalex.org/W2962886701","https://openalex.org/W3162637917","https://openalex.org/W2981979628","https://openalex.org/W2902842049","https://openalex.org/W2465112080","https://openalex.org/W3120700377","https://openalex.org/W3167196392","https://openalex.org/W3119106026","https://openalex.org/W2799004920","https://openalex.org/W3036226747","https://openalex.org/W2788657021","https://openalex.org/W2803526748","https://openalex.org/W2619042136","https://openalex.org/W2810781137","https://openalex.org/W3207855986","https://openalex.org/W3092989169","https://openalex.org/W2346211548","https://openalex.org/W3166703191","https://openalex.org/W2953096735","https://openalex.org/W2396730968"],"abstract_inverted_index":{"Graph":[0],"inference":[1],"methods":[2,31],"have":[3],"recently":[4],"attracted":[5],"a":[6,54,70,77],"great":[7],"interest":[8],"from":[9],"the":[10,15,28,43,107],"scientific":[11],"community,":[12],"due":[13],"to":[14,49],"large":[16],"value":[17],"they":[18],"bring":[19],"in":[20],"data":[21,38],"interpretation":[22],"and":[23,67,97,115],"analysis.":[24],"However,":[25],"most":[26],"of":[27,109],"available":[29,37],"state-of-the-art":[30],"focus":[32],"on":[33,81,112],"scenarios":[34],"where":[35],"all":[36],"can":[39,90],"be":[40],"explained":[41],"through":[42],"same":[44],"graph,":[45],"or":[46],"groups":[47],"corresponding":[48,95],"each":[50],"graph":[51],"are":[52],"known":[53],"priori.":[55],"In":[56],"this":[57,62],"paper,":[58],"we":[59,68],"argue":[60],"that":[61,89,101],"is":[63],"not":[64],"always":[65],"realistic":[66],"introduce":[69],"generative":[71],"model":[72],"for":[73],"mixed":[74],"signals":[75,93],"following":[76],"heat":[78],"diffusion":[79],"process":[80],"multiple":[82,99],"graphs.":[83],"We":[84,105],"propose":[85],"an":[86],"expectation-maximisation":[87],"algorithm":[88],"successfully":[91],"separate":[92],"into":[94],"groups,":[96],"infer":[98],"graphs":[100],"govern":[102],"their":[103],"behaviour.":[104],"demonstrate":[106],"benefits":[108],"our":[110],"method":[111],"both":[113],"synthetic":[114],"real":[116],"data.":[117]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
