{"id":"https://openalex.org/W4309614084","doi":"https://doi.org/10.3389/fdata.2022.1044709","title":"Modern Hopfield Networks for graph embedding","display_name":"Modern Hopfield Networks for graph embedding","publication_year":2022,"publication_date":"2022-11-17","ids":{"openalex":"https://openalex.org/W4309614084","doi":"https://doi.org/10.3389/fdata.2022.1044709","pmid":"https://pubmed.ncbi.nlm.nih.gov/36466714"},"language":"en","primary_location":{"id":"doi:10.3389/fdata.2022.1044709","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2022.1044709","pdf_url":"https://www.frontiersin.org/articles/10.3389/fdata.2022.1044709/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/articles/10.3389/fdata.2022.1044709/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101508037","display_name":"Yuchen Liang","orcid":"https://orcid.org/0009-0000-3111-0593"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuchen Liang","raw_affiliation_strings":["Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, United States","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038446902","display_name":"Dmitry Krotov","orcid":"https://orcid.org/0000-0001-6810-1906"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dmitry Krotov","raw_affiliation_strings":["MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, United States","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019559411","display_name":"Mohammed J. Zaki","orcid":"https://orcid.org/0000-0003-4711-0234"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammed J. Zaki","raw_affiliation_strings":["Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, United States","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101508037"],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":0.5249,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.71205216,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"5","issue":null,"first_page":"1044709","last_page":"1044709"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994000196456909,"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":0.9994000196456909,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9984999895095825,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9803000092506409,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7787007689476013},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6180739998817444},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5915119647979736},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5391855835914612},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5000748634338379},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.47257888317108154},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.47194045782089233},{"id":"https://openalex.org/keywords/hopfield-network","display_name":"Hopfield network","score":0.46751195192337036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45415300130844116},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4190274775028229},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.41691166162490845},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.3218613862991333},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2692731022834778},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13311856985092163}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7787007689476013},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6180739998817444},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5915119647979736},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5391855835914612},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5000748634338379},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.47257888317108154},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.47194045782089233},{"id":"https://openalex.org/C46421273","wikidata":"https://www.wikidata.org/wiki/Q1407668","display_name":"Hopfield network","level":3,"score":0.46751195192337036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45415300130844116},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4190274775028229},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.41691166162490845},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.3218613862991333},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2692731022834778},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13311856985092163},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/fdata.2022.1044709","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2022.1044709","pdf_url":"https://www.frontiersin.org/articles/10.3389/fdata.2022.1044709/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},{"id":"pmid:36466714","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36466714","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in big data","raw_type":null},{"id":"pmh:oai:doaj.org/article:38c58e98138a4281abfcd2505e5c429b","is_oa":true,"landing_page_url":"https://doaj.org/article/38c58e98138a4281abfcd2505e5c429b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Big Data, Vol 5 (2022)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:9713410","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9713410","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Front Big Data","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3389/fdata.2022.1044709","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2022.1044709","pdf_url":"https://www.frontiersin.org/articles/10.3389/fdata.2022.1044709/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309500","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4309614084.pdf","grobid_xml":"https://content.openalex.org/works/W4309614084.grobid-xml"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W637153065","https://openalex.org/W1614298861","https://openalex.org/W2009233867","https://openalex.org/W2034618876","https://openalex.org/W2056562706","https://openalex.org/W2059988607","https://openalex.org/W2070232376","https://openalex.org/W2080792322","https://openalex.org/W2090891622","https://openalex.org/W2092750499","https://openalex.org/W2099438806","https://openalex.org/W2102907934","https://openalex.org/W2110163023","https://openalex.org/W2112246162","https://openalex.org/W2118585731","https://openalex.org/W2128084896","https://openalex.org/W2132914434","https://openalex.org/W2135957668","https://openalex.org/W2154851992","https://openalex.org/W2154872931","https://openalex.org/W2156483112","https://openalex.org/W2188408756","https://openalex.org/W2294347342","https://openalex.org/W2387462954","https://openalex.org/W2393319904","https://openalex.org/W2405933695","https://openalex.org/W2587333550","https://openalex.org/W2700550412","https://openalex.org/W2804381853","https://openalex.org/W2917858269","https://openalex.org/W2960658350","https://openalex.org/W2962756421","https://openalex.org/W2964015378","https://openalex.org/W2964311892","https://openalex.org/W3037428919","https://openalex.org/W3042313988","https://openalex.org/W3047517563","https://openalex.org/W3049640930","https://openalex.org/W3102889924","https://openalex.org/W3104097132","https://openalex.org/W3106039696","https://openalex.org/W3133823961","https://openalex.org/W3177399989","https://openalex.org/W3182042768","https://openalex.org/W4207080266","https://openalex.org/W4212774754","https://openalex.org/W4247551517","https://openalex.org/W4248949446","https://openalex.org/W4291474301","https://openalex.org/W4294170691","https://openalex.org/W4297748376","https://openalex.org/W4298069009","https://openalex.org/W4299823361","https://openalex.org/W6677656871","https://openalex.org/W6682644385","https://openalex.org/W6684128514","https://openalex.org/W6690230747","https://openalex.org/W6758192066","https://openalex.org/W6769077657","https://openalex.org/W6936242187","https://openalex.org/W6947939616"],"related_works":["https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2539013788","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W2075222291"],"abstract_inverted_index":{"The":[0,82,121],"network":[1,11,44,61,87],"embedding":[2,45],"task":[3],"is":[4,103],"to":[5,91,127],"represent":[6],"a":[7,10,13,31,43,48],"node":[8,69,114],"in":[9,79],"as":[12,77,113],"low-dimensional":[14],"vector":[15],"while":[16],"incorporating":[17],"the":[18,65,80,86,93,128],"topological":[19],"and":[20,70,118,133],"structural":[21],"information.":[22],"Most":[23],"existing":[24],"approaches":[25],"solve":[26],"this":[27,39],"problem":[28],"by":[29],"factorizing":[30],"proximity":[32],"matrix,":[33],"either":[34],"directly":[35],"or":[36],"implicitly.":[37],"In":[38],"work,":[40],"we":[41],"introduce":[42],"method":[46,102],"from":[47],"new":[49],"perspective,":[50],"which":[51],"leverages":[52],"Modern":[53],"Hopfield":[54],"Networks":[55],"(MHN)":[56],"for":[57,109],"associative":[58],"learning.":[59],"Our":[60,100],"learns":[62],"associations":[63,75],"between":[64],"content":[66],"of":[67,85],"each":[68],"that":[71,97],"node's":[72,98],"neighbors.":[73,99],"These":[74],"serve":[76],"memories":[78],"MHN.":[81],"recurrent":[83],"dynamics":[84],"make":[88],"it":[89],"possible":[90],"recover":[92],"masked":[94],"node,":[95],"given":[96],"proposed":[101],"evaluated":[104],"on":[105],"different":[106],"benchmark":[107],"datasets":[108],"downstream":[110],"tasks":[111],"such":[112],"classification,":[115],"link":[116],"prediction,":[117],"graph":[119],"coarsening.":[120],"results":[122],"show":[123],"competitive":[124],"performance":[125],"compared":[126],"common":[129],"matrix":[130],"factorization":[131],"techniques":[132],"deep":[134],"learning":[135],"based":[136],"methods.":[137]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}