{"id":"https://openalex.org/W2734338834","doi":"https://doi.org/10.1109/ijcnn.2017.7965871","title":"Deep graph node kernels: A convex approach","display_name":"Deep graph node kernels: A convex approach","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2734338834","doi":"https://doi.org/10.1109/ijcnn.2017.7965871","mag":"2734338834"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7965871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965871","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/11567/881494","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045802198","display_name":"Luca Oneto","orcid":"https://orcid.org/0000-0002-8445-395X"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luca Oneto","raw_affiliation_strings":["DIBRIS Department, University of Genoa, Genova, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DIBRIS Department, University of Genoa, Genova, Italy","institution_ids":["https://openalex.org/I83816512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088959189","display_name":"Nicol\u00f2 Navarin","orcid":"https://orcid.org/0000-0002-4108-1754"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nicolo Navarin","raw_affiliation_strings":["Department of Mathematics, University of Padova, Padova, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Padova, Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064873591","display_name":"Alessandro Sperduti","orcid":"https://orcid.org/0000-0002-8686-850X"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alessandro Sperduti","raw_affiliation_strings":["Department of Mathematics, University of Padova, Padova, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Padova, Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036611143","display_name":"Davide Anguita","orcid":"https://orcid.org/0000-0001-7523-5291"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Davide Anguita","raw_affiliation_strings":["DIBRIS Department, University of Genoa, Genova, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DIBRIS Department, University of Genoa, Genova, Italy","institution_ids":["https://openalex.org/I83816512"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.07915779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"313","issue":null,"first_page":"316","last_page":"323"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9948999881744385,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9887999892234802,"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/exploit","display_name":"Exploit","score":0.8523045182228088},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7366974353790283},{"id":"https://openalex.org/keywords/graph-kernel","display_name":"Graph kernel","score":0.5681792497634888},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5512619018554688},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5314071774482727},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5231025218963623},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5095527172088623},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4645497500896454},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4515898525714874},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4478048086166382},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3954955041408539},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.23320767283439636},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.17268571257591248},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12632131576538086}],"concepts":[{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.8523045182228088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7366974353790283},{"id":"https://openalex.org/C100595998","wikidata":"https://www.wikidata.org/wiki/Q11731931","display_name":"Graph kernel","level":5,"score":0.5681792497634888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5512619018554688},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5314071774482727},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5231025218963623},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5095527172088623},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4645497500896454},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4515898525714874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4478048086166382},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3954955041408539},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.23320767283439636},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.17268571257591248},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12632131576538086},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn.2017.7965871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965871","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arpi.unipi.it:11568/962681","is_oa":false,"landing_page_url":"http://hdl.handle.net/11568/962681","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:iris.unige.it:11567/881494","is_oa":true,"landing_page_url":"http://hdl.handle.net/11567/881494","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:www.research.unipd.it:11577/3260068","is_oa":false,"landing_page_url":"http://hdl.handle.net/11577/3260068","pdf_url":null,"source":{"id":"https://openalex.org/S4306402547","display_name":"Padua Research Archive (University of Padova)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"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":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:iris.unige.it:11567/881494","is_oa":true,"landing_page_url":"http://hdl.handle.net/11567/881494","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W14071851","https://openalex.org/W1506859583","https://openalex.org/W1515020792","https://openalex.org/W1540155273","https://openalex.org/W1588401315","https://openalex.org/W1680392829","https://openalex.org/W1774304772","https://openalex.org/W1857382374","https://openalex.org/W1875445194","https://openalex.org/W1997716018","https://openalex.org/W2006151125","https://openalex.org/W2008857988","https://openalex.org/W2009059481","https://openalex.org/W2023533506","https://openalex.org/W2025768430","https://openalex.org/W2033590892","https://openalex.org/W2100495367","https://openalex.org/W2105295920","https://openalex.org/W2106525823","https://openalex.org/W2124637492","https://openalex.org/W2132773116","https://openalex.org/W2136922672","https://openalex.org/W2148603752","https://openalex.org/W2154851992","https://openalex.org/W2163922914","https://openalex.org/W2168029744","https://openalex.org/W2169847772","https://openalex.org/W2172000360","https://openalex.org/W2260355669","https://openalex.org/W2301541953","https://openalex.org/W2317435920","https://openalex.org/W2490023472","https://openalex.org/W2994602700","https://openalex.org/W2998216295","https://openalex.org/W2998768810","https://openalex.org/W3101749733","https://openalex.org/W3104097132","https://openalex.org/W3144619878","https://openalex.org/W4231109964","https://openalex.org/W4232932184","https://openalex.org/W4239510810","https://openalex.org/W4285719527","https://openalex.org/W6635310694","https://openalex.org/W6637386731","https://openalex.org/W6638285678","https://openalex.org/W6639065193","https://openalex.org/W6722680694","https://openalex.org/W7039177067","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W2089892314","https://openalex.org/W2363184354","https://openalex.org/W2179275589","https://openalex.org/W1983263273","https://openalex.org/W2121506664","https://openalex.org/W2189183545","https://openalex.org/W2170018389","https://openalex.org/W1558903433","https://openalex.org/W2974741803","https://openalex.org/W2093878082"],"abstract_inverted_index":{"Nowadays,":[0],"developing":[1],"effective":[2],"techniques":[3],"able":[4],"to":[5,40,138],"deal":[6,41],"with":[7,42],"data":[8],"coming":[9],"from":[10],"structured":[11,34],"domains":[12],"is":[13],"becoming":[14],"crucial.":[15],"In":[16,55],"this":[17,56,135],"context":[18],"kernel":[19,110],"methods":[20,47],"are":[21,128],"the":[22,73,78,81,89,92,97,125,140,155,158],"state-of-the-art":[23,93],"tool":[24],"widely":[25],"adopted":[26],"in":[27,114,130],"real-world":[28],"applications":[29],"that":[30],"involve":[31],"learning":[32,46],"on":[33,149],"data.":[35],"Contrarily,":[36],"when":[37],"one":[38,85],"has":[39],"unstructured":[43],"domains,":[44],"deep":[45,70,103,146],"represent":[48],"a":[49,60,69,102,106,131],"competitive,":[50],"or":[51],"even":[52],"better,":[53],"choice.":[54],"paper":[57],"we":[58,87,100],"propose":[59],"new":[61],"family":[62],"of":[63,72,80,91,108,124,144,157],"kernels":[64],"for":[65],"graphs":[66],"which":[67],"exploits":[68,77],"representation":[71],"information.":[74],"Our":[75],"proposal":[76],"advantages":[79],"two":[82],"worlds.":[83],"From":[84,96],"side":[86,99],"exploit":[88],"potentiality":[90],"graph":[94,152],"kernels.":[95],"other":[98],"develop":[101],"architecture":[104],"through":[105],"series":[107],"stacked":[109],"pre-image":[111],"estimators":[112],"trained":[113,129],"an":[115],"unsupervised":[116],"fashion":[117],"via":[118],"convex":[119],"optimization.":[120],"The":[121],"hidden":[122],"layers":[123],"proposed":[126],"framework":[127],"forward":[132],"manner":[133],"and":[134],"allows":[136],"us":[137],"avoid":[139],"greedy":[141],"layerwise":[142],"training":[143],"classical":[145],"learning.":[147],"Results":[148],"real":[150],"world":[151],"datasets":[153],"confirm":[154],"quality":[156],"proposal.":[159]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}