{"id":"https://openalex.org/W7128466179","doi":"https://doi.org/10.48550/arxiv.2602.07903","title":"GCN-MPPR: Enhancing the Propagation of Message Passing Neural Networks via Motif-Based Personalized PageRank","display_name":"GCN-MPPR: Enhancing the Propagation of Message Passing Neural Networks via Motif-Based Personalized PageRank","publication_year":2026,"publication_date":"2026-02-08","ids":{"openalex":"https://openalex.org/W7128466179","doi":"https://doi.org/10.48550/arxiv.2602.07903"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.07903","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125485304","display_name":"Mingcan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Mingcan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087247680","display_name":"Junchang Xin","orcid":"https://orcid.org/0000-0003-2077-8269"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin, Junchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013965378","display_name":"Zhongming Yao","orcid":"https://orcid.org/0009-0007-6463-9195"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Zhongming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009205410","display_name":"Kaifu Long","orcid":"https://orcid.org/0009-0007-6745-2473"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Long, Kaifu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125479279","display_name":"Zhiqiong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhiqiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15332115,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9000999927520752,"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":0.9000999927520752,"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":0.019500000402331352,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.0071000000461936,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/message-passing","display_name":"Message passing","score":0.8546000123023987},{"id":"https://openalex.org/keywords/pagerank","display_name":"PageRank","score":0.7833999991416931},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5235000252723694},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4837999939918518},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47699999809265137},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.41850000619888306}],"concepts":[{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.8546000123023987},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8282999992370605},{"id":"https://openalex.org/C2779172887","wikidata":"https://www.wikidata.org/wiki/Q184316","display_name":"PageRank","level":2,"score":0.7833999991416931},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5264000296592712},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5235000252723694},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4837999939918518},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.41850000619888306},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40059998631477356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3813000023365021},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.367900013923645},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3043000102043152},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.07903","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.07903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.07903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"pmh:doi:10.48550/arxiv.2602.07903","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"No poverty","score":0.6605378985404968,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,156,200],"algorithms":[1],"based":[2],"on":[3,9,125,170],"message":[4,140,148],"passing":[5,141,149],"neural":[6],"networks":[7],"(MPNNs)":[8],"graphs":[10],"have":[11],"recently":[12],"achieved":[13],"great":[14],"success":[15],"for":[16],"various":[17],"graph":[18],"applications.":[19],"However,":[20],"studies":[21],"find":[22],"that":[23,160,186],"these":[24,102],"methods":[25],"always":[26],"propagate":[27],"the":[28,45,67,89,96,118,126,134,139,147,161,168,177,198],"information":[29],"to":[30,39,49,58,116,123,138],"very":[31],"limited":[32,75,95],"neighborhoods":[33],"with":[34,193],"shallow":[35],"depth,":[36],"particularly":[37],"due":[38],"over-smoothing.":[40],"That":[41],"means":[42],"most":[43],"of":[44,70,85,91,98,107,120,128,143,167],"existing":[46],"MPNNs":[47,92],"fail":[48],"be":[50,181],"so":[51],"`deep'.":[52],"Although":[53],"some":[54],"previous":[55],"work":[56],"tended":[57],"handle":[59],"this":[60],"challenge":[61],"via":[62],"optimization-":[63],"or":[64],"structure-level":[65],"remedies,":[66],"overall":[68],"performance":[69,97],"GCNs":[71],"still":[72],"suffers":[73],"from":[74],"accuracy,":[76,171],"poor":[77],"stability,":[78,172],"and":[79,173],"unaffordable":[80],"computational":[81],"cost.":[82],"Moreover,":[83],"neglect":[84],"higher-order":[86,130],"relationships":[87],"during":[88],"propagation":[90],"has":[93],"further":[94],"them.":[99],"To":[100],"overcome":[101],"challenges,":[103],"a":[104,152,184],"novel":[105],"variant":[106],"PageRank":[108,112],"named":[109],"motif-based":[110],"personalized":[111],"(MPPR)":[113],"is":[114,136,204],"proposed":[115,162,178],"measure":[117],"influence":[119],"one":[121],"node":[122],"another":[124],"basis":[127],"considering":[129],"motif":[131],"relationships.":[132],"Secondly,":[133],"MPPR":[135],"utilized":[137],"process":[142,150],"GCNs,":[144],"thereby":[145],"guiding":[146],"at":[151],"relatively":[153],"`high'":[154],"level.":[155],"experimental":[157],"results":[158],"show":[159],"method":[163,179],"outperforms":[164],"almost":[165,189],"all":[166,190],"baselines":[169],"time":[174],"consumption.":[175],"Additionally,":[176],"can":[180,187],"considered":[182],"as":[183],"component":[185],"underpin":[188],"GCN":[191],"tasks,":[192],"DGCRL":[194],"being":[195],"demonstrated":[196],"in":[197],"experiment.":[199],"anonymous":[201],"code":[202],"repository":[203],"available":[205],"at:":[206],"https://anonymous.4open.science/r/GCN-MPPR-AFD6/.":[207]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-11T00:00:00"}