{"id":"https://openalex.org/W3026900506","doi":"https://doi.org/10.1109/tcyb.2020.2988791","title":"DigGCN: Learning Compact Graph Convolutional Networks via Diffusion Aggregation","display_name":"DigGCN: Learning Compact Graph Convolutional Networks via Diffusion Aggregation","publication_year":2020,"publication_date":"2020-05-18","ids":{"openalex":"https://openalex.org/W3026900506","doi":"https://doi.org/10.1109/tcyb.2020.2988791","mag":"3026900506","pmid":"https://pubmed.ncbi.nlm.nih.gov/32452792"},"language":"en","primary_location":{"id":"doi:10.1109/tcyb.2020.2988791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2020.2988791","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Cybernetics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5008708386","display_name":"Minglong Lei","orcid":"https://orcid.org/0000-0003-4406-8747"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Minglong Lei","raw_affiliation_strings":["Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing, China","Beijing Artificial Intelligence Institute, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]},{"raw_affiliation_string":"Beijing Artificial Intelligence Institute, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100414014","display_name":"Pei Quan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pei Quan","raw_affiliation_strings":["School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101473539","display_name":"Rongrong Ma","orcid":"https://orcid.org/0000-0003-4800-074X"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongrong Ma","raw_affiliation_strings":["School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113542097","display_name":"Yong Shi","orcid":"https://orcid.org/0000-0001-7974-1079"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Shi","raw_affiliation_strings":["School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108615648","display_name":"Lingfeng Niu","orcid":"https://orcid.org/0000-0002-5827-8449"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingfeng Niu","raw_affiliation_strings":["School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5008708386"],"corresponding_institution_ids":["https://openalex.org/I37796252","https://openalex.org/I4210100255"],"apc_list":null,"apc_paid":null,"fwci":1.6313,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.87180212,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"52","issue":"2","first_page":"912","last_page":"924"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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":0.9997000098228455,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9944999814033508,"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":0.9811000227928162,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7070660591125488},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5533834099769592},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5416437387466431},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5331928730010986},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5280402898788452},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.50943523645401},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4690421521663666},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.46347248554229736},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.45532894134521484},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.453694611787796},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42816710472106934},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42166414856910706},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3742864727973938},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3724287748336792},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.29571056365966797},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18853867053985596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7070660591125488},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5533834099769592},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5416437387466431},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5331928730010986},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5280402898788452},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.50943523645401},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4690421521663666},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.46347248554229736},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.45532894134521484},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.453694611787796},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42816710472106934},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42166414856910706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3742864727973938},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3724287748336792},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29571056365966797},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18853867053985596},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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":[{"descriptor_ui":"D004058","descriptor_name":"Diffusion","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004058","descriptor_name":"Diffusion","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004058","descriptor_name":"Diffusion","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/tcyb.2020.2988791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2020.2988791","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Cybernetics","raw_type":"journal-article"},{"id":"pmid:32452792","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32452792","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":"IEEE transactions on cybernetics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1658396367","display_name":null,"funder_award_id":"61672065","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1843357863","display_name":null,"funder_award_id":"11671379","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3907981520","display_name":null,"funder_award_id":"11991021","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W1992276332","https://openalex.org/W2008156351","https://openalex.org/W2022704179","https://openalex.org/W2041038237","https://openalex.org/W2074682976","https://openalex.org/W2095705004","https://openalex.org/W2098729277","https://openalex.org/W2116341502","https://openalex.org/W2118585731","https://openalex.org/W2139906443","https://openalex.org/W2149288670","https://openalex.org/W2153959628","https://openalex.org/W2154851992","https://openalex.org/W2156718197","https://openalex.org/W2314675936","https://openalex.org/W2393319904","https://openalex.org/W2405933695","https://openalex.org/W2460144244","https://openalex.org/W2520760693","https://openalex.org/W2566971417","https://openalex.org/W2755855437","https://openalex.org/W2788919350","https://openalex.org/W2797372563","https://openalex.org/W2808168148","https://openalex.org/W2905138462","https://openalex.org/W2907492528","https://openalex.org/W2962689221","https://openalex.org/W2962756421","https://openalex.org/W2963415211","https://openalex.org/W2964051675","https://openalex.org/W2964088520","https://openalex.org/W2970738028","https://openalex.org/W3104097132","https://openalex.org/W3125842778","https://openalex.org/W3148981562","https://openalex.org/W4244393449","https://openalex.org/W4293406386","https://openalex.org/W6600213771","https://openalex.org/W6637178625","https://openalex.org/W6674330103","https://openalex.org/W6674731556","https://openalex.org/W6677656871","https://openalex.org/W6685350579","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6730382033","https://openalex.org/W6732461846","https://openalex.org/W6736685754","https://openalex.org/W6738964360","https://openalex.org/W6741783443","https://openalex.org/W6743958420","https://openalex.org/W6745537798","https://openalex.org/W6748799445","https://openalex.org/W6748848838","https://openalex.org/W6752110883","https://openalex.org/W6753331806","https://openalex.org/W6754030515","https://openalex.org/W6754484989","https://openalex.org/W6754929296","https://openalex.org/W6755573351","https://openalex.org/W6763485134","https://openalex.org/W7015881123"],"related_works":["https://openalex.org/W2989932438","https://openalex.org/W3081496756","https://openalex.org/W2795435272","https://openalex.org/W2951851447","https://openalex.org/W3099765033","https://openalex.org/W2767651786","https://openalex.org/W2753840555","https://openalex.org/W3094605108","https://openalex.org/W4287763734","https://openalex.org/W3035116611"],"abstract_inverted_index":{"Recent":[0],"interests":[1],"in":[2,16,90],"graph":[3,95,102],"neural":[4,113],"networks":[5,114],"(GNNs)":[6],"have":[7,74],"received":[8],"increasing":[9],"concerns":[10],"due":[11,78],"to":[12,61,73,79,122,171,182],"their":[13],"superior":[14],"ability":[15],"the":[17,38,44,51,68,80,85,101,112,129,135,150,184,204,217],"network":[18,45,97,161,206],"embedding":[19,207],"field.":[20],"The":[21,118,160],"GNNs":[22],"typically":[23],"follow":[24],"a":[25,75,93,193,198],"message":[26],"passing":[27],"scheme":[28],"and":[29,49,109,127,138,213],"represent":[30],"nodes":[31],"by":[32,142,196],"aggregating":[33],"features":[34],"from":[35,124],"neighbors.":[36,180],"However,":[37],"current":[39],"aggregation":[40,69],"methods":[41,70,208],"assume":[42],"that":[43,167],"structure":[46],"is":[47],"static":[48],"define":[50],"local":[52],"receptive":[53,103,176],"fields":[54,104,177],"under":[55,209],"visible":[56],"connections,":[57],"which":[58,99],"consequently":[59],"fails":[60],"consider":[62],"latent":[63,130],"or":[64],"high-order":[65,136],"structures.":[66],"Besides,":[67],"are":[71,153],"known":[72],"depth":[76],"dilemma":[77],"over-smoothness":[81],"issues.":[82],"To":[83],"solve":[84],"above":[86],"shortcomings,":[87],"we":[88,133,190],"present":[89],"this":[91],"article":[92],"compact":[94],"convolutional":[96],"framework":[98],"defines":[100],"based":[105,154],"on":[106,155],"diffusion":[107,140,143,151],"paths":[108,141],"explicitly":[110],"compresses":[111],"with":[115,146,178,203],"sparsity":[116],"regularization.":[117],"proposed":[119],"model":[120,195],"seeks":[121],"learn":[123],"invisible":[125],"connections":[126],"recover":[128],"proximity.":[131],"First,":[132],"infer":[134],"proximity":[137],"construct":[139],"samplings.":[144],"Compared":[145],"random":[147],"walk":[148],"samplings,":[149],"samplings":[152],"regions":[156],"instead":[157],"of":[158,219],"paths.":[159],"inference":[162],"then":[163],"obtains":[164],"accurate":[165],"weights":[166],"can":[168],"be":[169],"leveraged":[170],"build":[172],"small":[173],"but":[174],"informative":[175],"salient":[179],"Second,":[181],"utilize":[183],"deep":[185],"information":[186],"while":[187],"avoiding":[188],"overfitting,":[189],"propose":[191],"learning":[192,212],"lightweight":[194],"introducing":[197],"nonconvex":[199],"regularizer.":[200],"Numerical":[201],"comparisons":[202],"existing":[205],"unsupervised":[210],"feature":[211],"supervised":[214],"classification":[215],"show":[216],"effectiveness":[218],"our":[220],"model.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}