{"id":"https://openalex.org/W3154497962","doi":"https://doi.org/10.1145/3442381.3449929","title":"Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks","display_name":"Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3154497962","doi":"https://doi.org/10.1145/3442381.3449929","mag":"3154497962"},"language":"en","primary_location":{"id":"doi:10.1145/3442381.3449929","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449929","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 Web Conference 2021","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3442381.3449929","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110664771","display_name":"Mingqi Yang","orcid":"https://orcid.org/0009-0002-8581-3336"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingqi Yang","raw_affiliation_strings":["Dalian University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026378565","display_name":"Yanming Shen","orcid":"https://orcid.org/0000-0003-4108-0230"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanming Shen","raw_affiliation_strings":["Dalian University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087744818","display_name":"Heng Qi","orcid":"https://orcid.org/0000-0002-8770-3934"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng Qi","raw_affiliation_strings":["Dalian University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020527092","display_name":"Baocai Yin","orcid":"https://orcid.org/0000-0003-3121-1823"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baocai Yin","raw_affiliation_strings":["Dalian University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, China","institution_ids":["https://openalex.org/I27357992"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2058","last_page":"2068"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9890000224113464,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.963100016117096,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7045732140541077},{"id":"https://openalex.org/keywords/substructure","display_name":"Substructure","score":0.5822509527206421},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.5353676676750183},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5331961512565613},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5273391008377075},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38700753450393677},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3294260501861572}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7045732140541077},{"id":"https://openalex.org/C99679407","wikidata":"https://www.wikidata.org/wiki/Q56761637","display_name":"Substructure","level":2,"score":0.5822509527206421},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5353676676750183},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5331961512565613},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5273391008377075},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38700753450393677},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3294260501861572},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3442381.3449929","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449929","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 Web Conference 2021","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2206.05499","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.05499","pdf_url":"https://arxiv.org/pdf/2206.05499","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3442381.3449929","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449929","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 Web Conference 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1179283095","https://openalex.org/W2008857988","https://openalex.org/W2080635178","https://openalex.org/W2114704115","https://openalex.org/W2142498761","https://openalex.org/W2159156271","https://openalex.org/W2558748708","https://openalex.org/W2606202972","https://openalex.org/W2624431344","https://openalex.org/W2750821778","https://openalex.org/W2788919350","https://openalex.org/W2809343047","https://openalex.org/W2899379687","https://openalex.org/W2945568301","https://openalex.org/W2947665307","https://openalex.org/W2962810718","https://openalex.org/W2963984147","https://openalex.org/W2964051675","https://openalex.org/W2998496395","https://openalex.org/W6681029592"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W4312407344","https://openalex.org/W2894289927"],"abstract_inverted_index":{"For":[0],"learning":[1,139],"graph":[2,10,16,42,74,84,142,174],"representations,":[3],"not":[4,150],"all":[5],"detailed":[6],"structures":[7,19,52,92,205],"within":[8],"a":[9,77,118],"are":[11,26],"relevant":[12],"to":[13,48,56,72,85,105,121,164,199],"the":[14,34,82,110,123,127,145,153,190,204,208],"given":[15],"tasks.":[17],"Task-relevant":[18],"can":[20],"be":[21,46,54],"localized":[22],"or":[23,31,90,136,156],"sparse":[24],"which":[25,59],"only":[27],"involved":[28],"in":[29,117,194],"subgraphs":[30,37,80,108,166],"characterized":[32],"by":[33,152,207],"interactions":[35],"of":[36,79,81,129,192],"(a":[38],"hierarchical":[39,91,137],"perspective).":[40],"A":[41],"neural":[43],"network":[44],"should":[45],"able":[47],"efficiently":[49],"extract":[50,106,165],"task-relevant":[51,88],"and":[53,93,125,141,159],"invariant":[55],"irrelevant":[57],"parts,":[58],"is":[60,115,149,161],"challenging":[61],"for":[62],"general":[63],"message":[64],"passing":[65],"GNNs.":[66],"In":[67],"this":[68,98],"work,":[69],"we":[70,100],"propose":[71],"learn":[73],"representations":[75],"from":[76],"sequence":[78],"original":[83],"better":[86],"capture":[87],"substructures":[89],"skip":[94],"noisy":[95],"parts.":[96,131],"To":[97],"end,":[99],"design":[101],"soft-mask":[102,114,146,178],"GNN":[103,147],"layer":[104,148,196],"desired":[107],"through":[109],"mask":[111],"mechanism.":[112],"The":[113],"defined":[116],"continuous":[119],"space":[120],"maintain":[122],"differentiability":[124],"characterize":[126],"weights":[128],"different":[130],"Compared":[132],"with":[133,167],"existing":[134],"subgraph":[135],"representation":[138],"methods":[140],"pooling":[143],"operations,":[144],"limited":[151],"fixed":[154],"sample":[155],"drop":[157],"ratio,":[158],"therefore":[160],"more":[162],"flexible":[163],"arbitrary":[168],"sizes.":[169],"Extensive":[170],"experiments":[171],"on":[172],"public":[173],"benchmarks":[175],"show":[176],"that":[177],"mechanism":[179],"brings":[180],"performance":[181],"improvements.":[182],"And":[183],"it":[184],"also":[185],"provides":[186],"interpretability":[187],"where":[188],"visualizing":[189],"values":[191],"masks":[193],"each":[195],"allows":[197],"us":[198],"have":[200],"an":[201],"insight":[202],"into":[203],"learned":[206],"model.":[209]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
