{"id":"https://openalex.org/W4378508583","doi":"https://doi.org/10.48550/arxiv.2305.14000","title":"Node-wise Diffusion for Scalable Graph Learning","display_name":"Node-wise Diffusion for Scalable Graph Learning","publication_year":2023,"publication_date":"2023-05-23","ids":{"openalex":"https://openalex.org/W4378508583","doi":"https://doi.org/10.48550/arxiv.2305.14000"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2305.14000","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.14000","pdf_url":"https://arxiv.org/pdf/2305.14000","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.14000","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049939311","display_name":"Keke Huang","orcid":"https://orcid.org/0000-0003-3553-3424"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Huang, Keke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083397767","display_name":"Jing Tang","orcid":"https://orcid.org/0000-0001-7480-7710"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Jing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101415161","display_name":"Juncheng Liu","orcid":"https://orcid.org/0000-0002-5895-0581"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Juncheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040420455","display_name":"Renchi Yang","orcid":"https://orcid.org/0000-0002-7284-3096"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Renchi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010903591","display_name":"Xiaokui Xiao","orcid":"https://orcid.org/0000-0003-0914-4580"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Xiaokui","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5049939311"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T10203","display_name":"Recommender Systems and Techniques","score":0.9847999811172485,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.941100001335144,"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":0.777411937713623},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7099522352218628},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6245402097702026},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5977796316146851},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5264290571212769},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47381746768951416},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43562671542167664},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.43208563327789307},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4217525124549866},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32052984833717346},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.13274216651916504}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.777411937713623},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7099522352218628},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6245402097702026},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5977796316146851},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5264290571212769},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47381746768951416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43562671542167664},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.43208563327789307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4217525124549866},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32052984833717346},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.13274216651916504},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2305.14000","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.14000","pdf_url":"https://arxiv.org/pdf/2305.14000","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2305.14000","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.14000","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2305.14000","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.14000","pdf_url":"https://arxiv.org/pdf/2305.14000","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.699999988079071,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4378508583.pdf","grobid_xml":"https://content.openalex.org/works/W4378508583.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2807634898","https://openalex.org/W1692008701","https://openalex.org/W2942366970","https://openalex.org/W2597588799","https://openalex.org/W4360593462","https://openalex.org/W1588041347"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"shown":[5],"superior":[6],"performance":[7],"for":[8,39,65,137,157,169],"semi-supervised":[9],"learning":[10,139],"of":[11,24,34,69,116,179,194,203,221,223,227,232],"numerous":[12],"web":[13,19,180],"applications,":[14],"such":[15],"as":[16],"classification":[17],"on":[18,176,216,243],"services":[20],"and":[21,28,75,85,140,161,185,211,225],"pages,":[22],"analysis":[23],"online":[25],"social":[26,184],"networks,":[27],"recommendation":[29],"in":[30,42,60,71,96,119],"e-commerce.":[31],"The":[32],"state":[33],"the":[35,45,72,89,100,113,142,149,191,200,217,236,240],"art":[36],"derives":[37],"representations":[38,156],"all":[40],"nodes":[41,58,78,159,224],"graphs":[43,70],"following":[44],"same":[46],"diffusion":[47,109],"(message":[48],"passing)":[49],"model":[50,61],"without":[51],"discriminating":[52],"their":[53],"uniqueness.":[54],"However,":[55],"(i)":[56,154],"labeled":[57,158],"involved":[59],"training":[62,212],"usually":[63],"account":[64],"a":[66,106],"small":[67],"portion":[68],"semisupervised":[73,138],"setting,":[74],"(ii)":[76,162],"different":[77,81],"locate":[79],"at":[80],"graph":[82],"local":[83],"contexts":[84],"it":[86,153],"inevitably":[87],"degrades":[88],"representation":[90,171,209],"qualities":[91],"if":[92],"treating":[93],"them":[94],"undistinguishedly":[95],"diffusion.":[97],"To":[98],"address":[99],"above":[101],"issues,":[102],"we":[103,134],"develop":[104],"NDM,":[105],"universal":[107],"node-wise":[108],"model,":[110],"to":[111,126,230],"capture":[112],"unique":[114],"characteristics":[115],"each":[117],"node":[118,129,170],"diffusion,":[120],"by":[121],"which":[122],"NDM":[123,136],"is":[124],"able":[125],"yield":[127],"high-quality":[128],"representations.":[130],"In":[131,145,205],"what":[132],"follows,":[133],"customize":[135],"design":[141],"NIGCN":[143,147,195,207],"model.":[144],"particular,":[146,206],"advances":[148],"efficiency":[150],"significantly":[151],"since":[152],"produces":[155],"only":[160,189],"adopts":[163],"well-designed":[164],"neighbor":[165],"sampling":[166],"techniques":[167],"tailored":[168],"generation.":[172],"Extensive":[173],"experimental":[174],"results":[175],"various":[177],"types":[178],"datasets,":[181],"including":[182],"citation,":[183],"co-purchasing":[186],"graphs,":[187],"not":[188],"verify":[190],"state-of-the-art":[192],"effectiveness":[193],"but":[196],"also":[197],"strongly":[198],"support":[199],"remarkable":[201],"scalability":[202],"NIGCN.":[204],"completes":[208],"generation":[210],"within":[213],"10":[214],"seconds":[215],"dataset":[218],"with":[219],"hundreds":[220],"millions":[222],"billions":[226],"edges,":[228],"up":[229],"orders":[231],"magnitude":[233],"speedups":[234],"over":[235],"baselines,":[237],"while":[238],"achieving":[239],"highest":[241],"F1-scores":[242],"classification.":[244]},"counts_by_year":[],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}
