{"id":"https://openalex.org/W3015814645","doi":"https://doi.org/10.1109/icassp40776.2020.9054574","title":"Spatial Gating Strategies for Graph Recurrent Neural Networks","display_name":"Spatial Gating Strategies for Graph Recurrent Neural Networks","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3015814645","doi":"https://doi.org/10.1109/icassp40776.2020.9054574","mag":"3015814645"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9054574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5036583414","display_name":"Luana Ruiz","orcid":"https://orcid.org/0000-0002-9666-1211"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Luana Ruiz","raw_affiliation_strings":["Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069506165","display_name":"Fernando Gama","orcid":"https://orcid.org/0000-0001-6117-8193"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fernando Gama","raw_affiliation_strings":["Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078862959","display_name":"Alejandro Ribeiro","orcid":"https://orcid.org/0000-0003-4230-9906"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alejandro Ribeiro","raw_affiliation_strings":["Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036583414"],"corresponding_institution_ids":["https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.03596855,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5550","last_page":"5554"},"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9955000281333923,"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"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9904999732971191,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7275274395942688},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6193103194236755},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4650382399559021},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.45574167370796204},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4496668577194214},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36677443981170654}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7275274395942688},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6193103194236755},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4650382399559021},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45574167370796204},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4496668577194214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36677443981170654}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9054574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5400000214576721,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W391985582","https://openalex.org/W1522301498","https://openalex.org/W2101491865","https://openalex.org/W2235146554","https://openalex.org/W2299462150","https://openalex.org/W2402302915","https://openalex.org/W2565330852","https://openalex.org/W2616297073","https://openalex.org/W2756203131","https://openalex.org/W2781888589","https://openalex.org/W2790871760","https://openalex.org/W2792433162","https://openalex.org/W2798598284","https://openalex.org/W2943959761","https://openalex.org/W2963241951","https://openalex.org/W2963358464","https://openalex.org/W2963674505","https://openalex.org/W2963858333","https://openalex.org/W2964121744","https://openalex.org/W2984479486","https://openalex.org/W2986508977","https://openalex.org/W3103720336","https://openalex.org/W4297733535","https://openalex.org/W6631190155","https://openalex.org/W6712909570","https://openalex.org/W6746015598","https://openalex.org/W6749077313","https://openalex.org/W6762304760"],"related_works":["https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W4225394202","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W3017902212","https://openalex.org/W2964335273","https://openalex.org/W2982145560","https://openalex.org/W2969450769"],"abstract_inverted_index":{"Graph":[0],"Recurrent":[1],"Neural":[2],"Networks":[3],"(GRNNs)":[4],"are":[5,17,72,113],"a":[6,120,125],"neural":[7,27],"network":[8],"architecture":[9],"devised":[10],"to":[11,24,115],"learn":[12],"from":[13],"graph":[14,21,67,77],"processes,":[15],"which":[16],"time":[18,59],"sequences":[19],"of":[20,33,58,106,128],"signals.":[22],"Similarly":[23],"traditional":[25],"recurrent":[26],"networks,":[28],"GRNNs":[29,99,112,118],"experience":[30],"the":[31,49,56,76,101,107],"problem":[32,127],"vanishing/exploding":[34],"gradients":[35],"when":[36],"learning":[37],"long":[38,69],"term":[39,70],"causal":[40],"dependencies.":[41],"When":[42],"these":[43],"dependencies":[44,71],"do":[45],"not":[46],"depend":[47],"on":[48],"graph,":[50],"this":[51],"issue":[52],"is":[53],"solved":[54],"by":[55,75],"addition":[57],"gates":[60],"(long":[61],"short-term":[62],"memory":[63],"architectures).":[64],"However,":[65],"in":[66,119],"processes":[68],"directly":[73],"influenced":[74],"structure,":[78],"and":[79,103,110],"can":[80],"be":[81],"stronger":[82],"or":[83],"weaker":[84],"across":[85],"certain":[86],"node":[87,102],"paths.":[88],"To":[89],"address":[90],"this,":[91],"we":[92],"propose":[93],"two":[94],"spatial":[95],"gating":[96],"strategies":[97],"for":[98],"leveraging":[100],"edge":[104],"structure":[105],"graph.":[108],"Node":[109],"edgegated":[111],"shown":[114],"outperform":[116],"other":[117],"synthetic":[121],"as":[122,124],"well":[123],"real-world":[126],"earthquake":[129],"epicenter":[130],"prediction.":[131]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}