{"id":"https://openalex.org/W4224315655","doi":"https://doi.org/10.1145/3485447.3512171","title":"Dual-branch Density Ratio Estimation for Signed Network Embedding","display_name":"Dual-branch Density Ratio Estimation for Signed Network Embedding","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4224315655","doi":"https://doi.org/10.1145/3485447.3512171"},"language":"en","primary_location":{"id":"doi:10.1145/3485447.3512171","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3485447.3512171","pdf_url":null,"source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","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/A5020365097","display_name":"Pinghua Xu","orcid":"https://orcid.org/0000-0002-4197-9337"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pinghua Xu","raw_affiliation_strings":["Wuhan University, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074672983","display_name":"Yibing Zhan","orcid":"https://orcid.org/0000-0003-3180-0484"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibing Zhan","raw_affiliation_strings":["JD Explore Academy, China"],"affiliations":[{"raw_affiliation_string":"JD Explore Academy, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100324286","display_name":"Liu Liu","orcid":"https://orcid.org/0000-0002-8128-2788"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Liu Liu","raw_affiliation_strings":["The University of Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085309099","display_name":"Baosheng Yu","orcid":"https://orcid.org/0000-0002-0761-7893"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Baosheng Yu","raw_affiliation_strings":["The University of Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060042752","display_name":"Bo Du","orcid":"https://orcid.org/0000-0002-0059-8458"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Du","raw_affiliation_strings":["Wuhan University, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007475662","display_name":"Jia Wu","orcid":"https://orcid.org/0000-0002-1371-5801"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jia Wu","raw_affiliation_strings":["Macquarie University, Australia"],"affiliations":[{"raw_affiliation_string":"Macquarie University, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101547511","display_name":"Wenbin Hu","orcid":"https://orcid.org/0000-0002-9258-3850"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbin Hu","raw_affiliation_strings":["Wuhan University, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5020365097"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":0.5197,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.61129067,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1651","last_page":"1662"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9950000047683716,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9843999743461609,"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":0.7262540459632874},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49071967601776123},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4680442810058594},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.43083009123802185},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4130028784275055},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3651773929595947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36282676458358765},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2575575113296509}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7262540459632874},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49071967601776123},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4680442810058594},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.43083009123802185},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4130028784275055},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3651773929595947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36282676458358765},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2575575113296509},{"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3485447.3512171","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3485447.3512171","pdf_url":null,"source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7028941803","display_name":null,"funder_award_id":"61976162,82174230,62002090","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1991408655","https://openalex.org/W2073415627","https://openalex.org/W2142517301","https://openalex.org/W2154851992","https://openalex.org/W2585835859","https://openalex.org/W2595947069","https://openalex.org/W2622849676","https://openalex.org/W2759848268","https://openalex.org/W2765811365","https://openalex.org/W2783466287","https://openalex.org/W2788045146","https://openalex.org/W2887092413","https://openalex.org/W2896802857","https://openalex.org/W2896852898","https://openalex.org/W2905267911","https://openalex.org/W2914833637","https://openalex.org/W2951533109","https://openalex.org/W2962756421","https://openalex.org/W2962997783","https://openalex.org/W2964140784","https://openalex.org/W2985331920","https://openalex.org/W2997638284","https://openalex.org/W3012659934","https://openalex.org/W3033061629","https://openalex.org/W3034402203","https://openalex.org/W3080456792","https://openalex.org/W3099565317","https://openalex.org/W3103311102","https://openalex.org/W3104097132","https://openalex.org/W3169236624","https://openalex.org/W3175507592","https://openalex.org/W4291474301"],"related_works":["https://openalex.org/W2051487156","https://openalex.org/W2081900870","https://openalex.org/W2073681303","https://openalex.org/W2037549926","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2053286651","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2181743346"],"abstract_inverted_index":{"Signed":[0],"network":[1],"embedding":[2],"(SNE)":[3],"has":[4],"received":[5],"considerable":[6],"attention":[7],"in":[8,57],"recent":[9],"years.":[10],"A":[11],"mainstream":[12],"idea":[13],"of":[14,25,44,76,153],"SNE":[15],"is":[16,167],"to":[17,41,93,106],"learn":[18],"node":[19,116],"representations":[20],"by":[21],"estimating":[22],"the":[23,42,54,82,87,95,108,137],"ratio":[24,37,66,142],"sampling":[26,92,105],"densities.":[27],"Though":[28],"achieving":[29],"promising":[30],"performance,":[31],"these":[32],"methods":[33,138,154],"based":[34,139],"on":[35,119,140],"density":[36,65,141],"estimation":[38,67,143],"are":[39],"limited":[40],"issues":[43],"confusing":[45,83],"sample,":[46],"expected":[47,88,96],"error,":[48],"and":[49,98,115,122,162],"fixed":[50,109],"priori.":[51,110],"To":[52],"alleviate":[53,107],"above-mentioned":[55],"issues,":[56],"this":[58],"paper,":[59],"we":[60],"propose":[61],"a":[62,77],"novel":[63],"dual-branch":[64,78],"(DDRE)":[68],"architecture":[69],"for":[70],"SNE.":[71],"Specifically,":[72],"DDRE":[73,132],"1)":[74],"consists":[75],"network,":[79],"dealing":[80],"with":[81,150],"sample;":[84],"2)":[85],"proposes":[86],"matrix":[89],"factorization":[90],"without":[91],"avoid":[94],"error;":[97],"3)":[99],"devises":[100],"an":[101],"adaptive":[102],"cross":[103],"noise":[104],"We":[111],"perform":[112],"sign":[113],"prediction":[114],"classification":[117],"experiments":[118],"four":[120],"real-world":[121],"three":[123],"artificial":[124],"datasets,":[125],"respectively.":[126],"Extensive":[127],"empirical":[128],"results":[129],"demonstrate":[130],"that":[131],"not":[133],"only":[134],"significantly":[135],"outperforms":[136],"but":[144],"also":[145],"achieves":[146],"competitive":[147],"performance":[148],"compared":[149],"other":[151],"types":[152],"such":[155],"as":[156],"graph":[157,163],"likelihood,":[158],"generative":[159],"adversarial":[160],"networks,":[161],"convolutional":[164],"networks.":[165],"Code":[166],"publicly":[168],"available":[169],"at":[170],"https://github.com/WHU-SNA/DDRE.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
