{"id":"https://openalex.org/W3029158597","doi":"https://doi.org/10.1145/3398682.3400060","title":"Graph Learning with Loss-Guided Training","display_name":"Graph Learning with Loss-Guided Training","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3029158597","doi":"https://doi.org/10.1145/3398682.3400060","mag":"3029158597"},"language":"en","primary_location":{"id":"doi:10.1145/3398682.3400060","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3398682.3400060","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3398682.3400060","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences &amp; Systems (GRADES) and Network Data Analytics (NDA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3398682.3400060","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089144342","display_name":"Eliav Buchnik","orcid":null},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]},{"id":"https://openalex.org/I4210117425","display_name":"Google (Israel)","ror":"https://ror.org/02c20ys54","country_code":"IL","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210117425","https://openalex.org/I4210128969"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Eliav Buchnik","raw_affiliation_strings":["Tel Aviv University, Google Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tel Aviv University, Google Research","institution_ids":["https://openalex.org/I16391192","https://openalex.org/I4210117425"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026385549","display_name":"Edith Cohen","orcid":"https://orcid.org/0000-0002-3926-8237"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]},{"id":"https://openalex.org/I4210117425","display_name":"Google (Israel)","ror":"https://ror.org/02c20ys54","country_code":"IL","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210117425","https://openalex.org/I4210128969"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Edith Cohen","raw_affiliation_strings":["Google Research, Tel Aviv University","[Google Research, Tel Aviv University]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research, Tel Aviv University","institution_ids":["https://openalex.org/I16391192","https://openalex.org/I4210117425"]},{"raw_affiliation_string":"[Google Research, Tel Aviv University]","institution_ids":["https://openalex.org/I16391192","https://openalex.org/I4210117425"]}]}],"institutions":[],"countries_distinct_count":1,"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":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9995999932289124,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9987999796867371,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9987000226974487,"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":0.735501766204834},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6370663046836853},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5970818996429443},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5801610946655273},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.5777592062950134},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.540344774723053},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4721503257751465},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4605701267719269},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.45025742053985596},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.42701610922813416},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.42433232069015503},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4162042438983917},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3169960379600525},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.30153998732566833},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07181861996650696}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.735501766204834},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6370663046836853},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5970818996429443},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5801610946655273},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.5777592062950134},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.540344774723053},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4721503257751465},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4605701267719269},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.45025742053985596},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.42701610922813416},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.42433232069015503},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4162042438983917},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3169960379600525},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30153998732566833},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07181861996650696},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3398682.3400060","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3398682.3400060","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3398682.3400060","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences &amp; Systems (GRADES) and Network Data Analytics (NDA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2006.00460","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.00460","pdf_url":"https://arxiv.org/pdf/2006.00460","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:3029158597","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2006.00460","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":"doi:10.48550/arxiv.2006.00460","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2006.00460","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:10.1145/3398682.3400060","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3398682.3400060","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3398682.3400060","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences &amp; Systems (GRADES) and Network Data Analytics (NDA)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1510644948","display_name":null,"funder_award_id":"1595/19","funder_id":"https://openalex.org/F4320322252","funder_display_name":"Israel Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320322252","display_name":"Israel Science Foundation","ror":"https://ror.org/04sazxf24"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W168564468","https://openalex.org/W1577337550","https://openalex.org/W1842094663","https://openalex.org/W1979819093","https://openalex.org/W1980147176","https://openalex.org/W1994389483","https://openalex.org/W2013809345","https://openalex.org/W2024128809","https://openalex.org/W2034417563","https://openalex.org/W2045555847","https://openalex.org/W2054141820","https://openalex.org/W2072773380","https://openalex.org/W2085845250","https://openalex.org/W2095293504","https://openalex.org/W2096733369","https://openalex.org/W2099866409","https://openalex.org/W2101409192","https://openalex.org/W2108598243","https://openalex.org/W2153579005","https://openalex.org/W2153959628","https://openalex.org/W2154851992","https://openalex.org/W2174940656","https://openalex.org/W2177410802","https://openalex.org/W2295869408","https://openalex.org/W2296073425","https://openalex.org/W2315403234","https://openalex.org/W2341497066","https://openalex.org/W2468907370","https://openalex.org/W2519887557","https://openalex.org/W2807021761","https://openalex.org/W2907492528","https://openalex.org/W2950133940","https://openalex.org/W2962756421","https://openalex.org/W2963224980","https://openalex.org/W2963516811","https://openalex.org/W2963984147","https://openalex.org/W2964321699","https://openalex.org/W2976859544","https://openalex.org/W2997591727","https://openalex.org/W3099206234","https://openalex.org/W3100848837","https://openalex.org/W3104097132","https://openalex.org/W4210257598","https://openalex.org/W6696934833","https://openalex.org/W6719270105"],"related_works":["https://openalex.org/W2900838066","https://openalex.org/W2951364255","https://openalex.org/W3173547939","https://openalex.org/W2981924372","https://openalex.org/W3188343081","https://openalex.org/W1557623003","https://openalex.org/W2963476860","https://openalex.org/W3170937213","https://openalex.org/W3128963076","https://openalex.org/W3038628659","https://openalex.org/W3161019670","https://openalex.org/W3048627609","https://openalex.org/W2770298516","https://openalex.org/W3033335901","https://openalex.org/W3105423414","https://openalex.org/W3012674152","https://openalex.org/W2988959735","https://openalex.org/W1485800019","https://openalex.org/W83651405","https://openalex.org/W3208204210"],"abstract_inverted_index":{"Classically,":[0],"ML":[1],"models":[2],"trained":[3],"with":[4,67,88],"stochastic":[5],"gradient":[6],"descent":[7],"(SGD)":[8],"are":[9,98,109],"designed":[10],"to":[11],"minimize":[12],"the":[13,29,51,55,104,143],"average":[14],"loss":[15],"per":[16],"example":[17,114],"and":[18,39,90,107,154],"use":[19],"a":[20,75,134],"distribution":[21,53],"of":[22,31,57,78,93,137,150],"training":[23,52,58,61,73,95,126,152],"examples":[24,66,96],"that":[25,41,48,60,97,122],"remains":[26],"static":[27,145],"in":[28,34,54,74,127,148],"course":[30,56],"training.":[32],"Research":[33],"recent":[35],"years":[36],"demonstrated,":[37],"empirically":[38],"theoretically,":[40],"significant":[42,140],"acceleration":[43,141],"is":[44,62],"possible":[45],"by":[46,83],"methods":[47,81,86,121],"dynamically":[49],"adjust":[50],"so":[59],"more":[63],"focused":[64],"on":[65,103,133],"higher":[68],"loss.":[69],"We":[70,117],"explore":[71],"loss-guided":[72,125],"new":[76],"domain":[77],"node":[79],"embedding":[80],"pioneered":[82],"DeepWalk.":[84],"These":[85],"work":[87],"implicit":[89],"large":[91],"set":[92],"positive":[94],"generated":[99],"using":[100],"random":[101],"walks":[102],"input":[105],"graph":[106],"therefore":[108],"not":[110],"amenable":[111],"for":[112,124],"typical":[113],"selection":[115],"methods.":[116],"propose":[118],"computationally":[119],"efficient":[120],"allow":[123],"this":[128],"framework.":[129],"Our":[130],"empirical":[131],"evaluation":[132],"rich":[135],"collection":[136],"datasets":[138],"shows":[139],"over":[142],"baseline":[144],"methods,":[146],"both":[147],"terms":[149],"total":[151],"performed":[153],"overall":[155],"computation.":[156]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
