{"id":"https://openalex.org/W2768092833","doi":"https://doi.org/10.1145/3132847.3132900","title":"Enhancing the Network Embedding Quality with Structural Similarity","display_name":"Enhancing the Network Embedding Quality with Structural Similarity","publication_year":2017,"publication_date":"2017-11-06","ids":{"openalex":"https://openalex.org/W2768092833","doi":"https://doi.org/10.1145/3132847.3132900","mag":"2768092833"},"language":"en","primary_location":{"id":"doi:10.1145/3132847.3132900","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132900","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5073493925","display_name":"Tianshu Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianshu Lyu","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100368731","display_name":"Yuan Zhang","orcid":"https://orcid.org/0000-0002-7849-208X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Zhang","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100456144","display_name":"Yan Zhang","orcid":"https://orcid.org/0009-0005-1403-6092"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhang","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073493925"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":6.277,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.97107449,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"147","last_page":"156"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994999766349792,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9706000089645386,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7619130611419678},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7086189389228821},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5579784512519836},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.5426275730133057},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5395058989524841},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5360695719718933},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5253159999847412},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5224408507347107},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4986855983734131},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.49366864562034607},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4470149278640747},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44230619072914124},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.43648576736450195},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3350645899772644},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14300888776779175},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11517959833145142}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7619130611419678},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7086189389228821},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5579784512519836},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.5426275730133057},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5395058989524841},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5360695719718933},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5253159999847412},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5224408507347107},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4986855983734131},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.49366864562034607},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4470149278640747},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44230619072914124},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.43648576736450195},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3350645899772644},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14300888776779175},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11517959833145142},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3132847.3132900","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132900","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W348384746","https://openalex.org/W1888005072","https://openalex.org/W2001141328","https://openalex.org/W2008857988","https://openalex.org/W2028695285","https://openalex.org/W2053186076","https://openalex.org/W2079553537","https://openalex.org/W2088412871","https://openalex.org/W2089793534","https://openalex.org/W2090891622","https://openalex.org/W2097308346","https://openalex.org/W2105543219","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2155261478","https://openalex.org/W2157312249","https://openalex.org/W2163485494","https://openalex.org/W2168627253","https://openalex.org/W2393319904","https://openalex.org/W2607500032","https://openalex.org/W2962756421","https://openalex.org/W2997591727","https://openalex.org/W3104097132","https://openalex.org/W3105705953"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W2082479932","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Neural":[0],"network":[1,7,103,189],"techniques":[2],"are":[3],"widely":[4],"used":[5],"in":[6,21,147],"embedding,":[8],"boosting":[9],"the":[10,29,37,67,76,129,162,165,175],"result":[11],"of":[12,24,31,46,158],"node":[13,125,166],"classification,":[14],"link":[15],"prediction,":[16],"visualization":[17],"and":[18,26,40,80,120,150,186],"other":[19,184],"tasks":[20],"both":[22,117],"aspects":[23,136],"efficiency":[25],"quality.":[27,113,177],"All":[28],"state":[30],"art":[32],"algorithms":[33,191],"put":[34],"effort":[35],"on":[36,59,164,192],"neighborhood":[38],"information":[39,107,119],"try":[41],"to":[42,52,75,90,110,123,172],"make":[43],"full":[44],"use":[45],"it.":[47],"However,":[48],"it":[49],"is":[50,128],"hard":[51],"recognize":[53],"core":[54],"periphery":[55],"structures":[56],"simply":[57],"based":[58,72,86],"neighborhood.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64,140,154],"first":[65,130],"discuss":[66],"influence":[68],"brought":[69],"by":[70],"random-walk":[71,85],"sampling":[73,87],"strategies":[74,88],"embedding":[77,176,190],"results.":[78],"Theoretical":[79],"experimental":[81],"evidences":[82],"show":[83,179],"that":[84,101,132,180],"fail":[89],"fully":[91],"capture":[92],"structural":[93,106],"equivalence.":[94],"We":[95],"present":[96],"a":[97],"new":[98],"method,":[99],"SNS,":[100],"performs":[102],"embeddings":[104],"using":[105],"(namely":[108],"graphlets)":[109],"enhance":[111],"its":[112],"SNS":[114],"effectively":[115],"utilizes":[116],"neighbor":[118],"local-subgraphs":[121],"similarity":[122],"learn":[124],"embeddings.":[126],"This":[127],"framework":[131],"combines":[133],"these":[134],"two":[135,144],"as":[137,139],"far":[138],"know,":[141],"positively":[142],"merging":[143],"important":[145],"areas":[146],"graph":[148],"mining":[149],"machine":[151],"learning.":[152],"Moreover,":[153],"investigate":[155],"what":[156],"kinds":[157],"local-subgraph":[159],"features":[160],"matter":[161],"most":[163],"classification":[167],"task,":[168],"which":[169],"enables":[170],"us":[171],"further":[173],"improve":[174],"Experiments":[178],"our":[181],"algorithm":[182],"outperforms":[183],"unsupervised":[185],"semi-supervised":[187],"neural":[188],"several":[193],"real-world":[194],"datasets.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-01T17:29:45.350535","created_date":"2025-10-10T00:00:00"}
