{"id":"https://openalex.org/W4412825912","doi":"https://doi.org/10.1145/3711896.3736913","title":"Divergent Paths: Separating Homophilic and Heterophilic Learning for Enhanced Graph-level Representations","display_name":"Divergent Paths: Separating Homophilic and Heterophilic Learning for Enhanced Graph-level Representations","publication_year":2025,"publication_date":"2025-08-01","ids":{"openalex":"https://openalex.org/W4412825912","doi":"https://doi.org/10.1145/3711896.3736913"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3736913","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736913","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3711896.3736913","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101045021","display_name":"Lei Han","orcid":"https://orcid.org/0009-0000-2057-5408"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Han Lei","raw_affiliation_strings":["College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0009-0000-2057-5408","affiliations":[{"raw_affiliation_string":"College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029547119","display_name":"Jiaxing Xu","orcid":"https://orcid.org/0000-0003-2498-5812"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jiaxing Xu","raw_affiliation_strings":["College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0003-2498-5812","affiliations":[{"raw_affiliation_string":"College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081192614","display_name":"Xia Dong","orcid":"https://orcid.org/0000-0003-2412-3120"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Xia Dong","raw_affiliation_strings":["College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0003-2412-3120","affiliations":[{"raw_affiliation_string":"College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074491217","display_name":"Yiping Ke","orcid":"https://orcid.org/0000-0001-9473-3202"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yiping Ke","raw_affiliation_strings":["College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-9473-3202","affiliations":[{"raw_affiliation_string":"College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101045021"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":2.1733,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.89594201,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1286","last_page":"1295"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9984999895095825,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9983000159263611,"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/computer-science","display_name":"Computer science","score":0.6459537148475647},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46586278080940247},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.41350215673446655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32615137100219727}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6459537148475647},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46586278080940247},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.41350215673446655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32615137100219727}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3711896.3736913","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736913","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},{"id":"pmh:oai:dr.ntu.edu.sg:10356/200028","is_oa":false,"landing_page_url":"https://dl.acm.org/doi/proceedings/10.1145/3690624","pdf_url":null,"source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"}],"best_oa_location":{"id":"doi:10.1145/3711896.3736913","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736913","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2025167103","https://openalex.org/W2056562706","https://openalex.org/W2141303268","https://openalex.org/W2602856279","https://openalex.org/W2963017945","https://openalex.org/W2971865858","https://openalex.org/W3090999459","https://openalex.org/W3093499132","https://openalex.org/W3102554291","https://openalex.org/W3128443161","https://openalex.org/W3181189986","https://openalex.org/W3204146977","https://openalex.org/W3208638341","https://openalex.org/W3210034512","https://openalex.org/W4320060387","https://openalex.org/W4387521354","https://openalex.org/W4406237835","https://openalex.org/W6803400136"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Graph":[0],"Convolutional":[1],"Networks":[2],"(GCNs)":[3],"are":[4],"predominantly":[5],"tailored":[6],"for":[7,112],"graphs":[8,68],"displaying":[9],"homophily,":[10],"where":[11],"similar":[12],"nodes":[13],"connect,":[14],"but":[15],"often":[16],"fail":[17],"on":[18,53,67,200],"heterophilic":[19,32],"graphs.":[20],"The":[21,182],"strategy":[22],"of":[23,81,132,176],"adopting":[24],"distinct":[25,109],"approaches":[26],"to":[27,107,168],"learn":[28,123],"from":[29,100],"homophilic":[30],"and":[31,41,46,76,83,114,126,137,153,189,195],"components":[33,78],"in":[34,49,178,207],"node-level":[35],"tasks":[36],"has":[37],"been":[38],"widely":[39],"discussed":[40],"proven":[42],"effective":[43],"both":[44],"theoretically":[45],"experimentally.":[47],"However,":[48],"graph-level":[50,201],"tasks,":[51,202],"research":[52,63],"this":[54,60,119],"topic":[55],"remains":[56],"notably":[57],"scarce.":[58],"Addressing":[59],"gap,":[61],"our":[62],"conducts":[64],"an":[65,133,138],"analysis":[66],"with":[69,191],"nodes'":[70],"category":[71],"ID":[72],"available,":[73],"distinguishing":[74],"intra-category":[75,134],"inter-category":[77,101,115,127,139],"as":[79],"embodiment":[80],"homophily":[82],"heterophily,":[84],"respectively.":[85],"We":[86],"find":[87],"while":[88],"GCNs":[89],"excel":[90],"at":[91],"extracting":[92],"information":[93],"within":[94],"categories,":[95],"they":[96],"frequently":[97],"capture":[98],"noise":[99],"components.":[102,181],"Consequently,":[103],"it":[104],"is":[105,146],"crucial":[106],"employ":[108],"learning":[110],"strategies":[111],"intra-":[113,125],"elements.":[116],"To":[117],"alleviate":[118],"problem,":[120],"we":[121,163],"separately":[122],"the":[124,161,170,174,179,187],"parts":[128],"by":[129,148],"a":[130,154,165,192],"combination":[131],"convolution":[135,142],"(IntraNet)":[136],"high-pass":[140,166],"graph":[141,150,157],"(InterNet).":[143],"Our":[144],"IntraNet":[145,188],"supported":[147],"sophisticated":[149],"preprocessing":[151],"steps":[152],"novel":[155],"category-based":[156],"readout":[158],"function.":[159],"For":[160],"InterNet,":[162],"utilize":[164],"filter":[167],"amplify":[169],"node":[171],"disparities,":[172],"enhancing":[173],"recognition":[175],"details":[177],"high-frequency":[180],"proposed":[183],"approach,":[184],"DivGNN,":[185],"combines":[186],"InterNet":[190],"gated":[193],"mechanism":[194],"substantially":[196],"improves":[197],"classification":[198],"performance":[199],"surpassing":[203],"traditional":[204],"GNN":[205],"baselines":[206],"effectiveness.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
