{"id":"https://openalex.org/W3008467772","doi":"https://doi.org/10.1109/bigdata47090.2019.9006299","title":"Integrating Local Vertex/Edge Embedding via Deep Matrix Fusion and Siamese Multi-label Classification","display_name":"Integrating Local Vertex/Edge Embedding via Deep Matrix Fusion and Siamese Multi-label Classification","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008467772","doi":"https://doi.org/10.1109/bigdata47090.2019.9006299","mag":"3008467772"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006299","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006299","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5044123636","display_name":"Yang Zhou","orcid":"https://orcid.org/0000-0001-7839-4933"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yang Zhou","raw_affiliation_strings":["Auburn University"],"affiliations":[{"raw_affiliation_string":"Auburn University","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073746065","display_name":"Chao Jiang","orcid":"https://orcid.org/0000-0002-0467-6177"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chao Jiang","raw_affiliation_strings":["Auburn University"],"affiliations":[{"raw_affiliation_string":"Auburn University","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100673890","display_name":"Zijie Zhang","orcid":"https://orcid.org/0000-0003-1254-098X"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zijie Zhang","raw_affiliation_strings":["Auburn University"],"affiliations":[{"raw_affiliation_string":"Auburn University","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021332177","display_name":"Dejing Dou","orcid":"https://orcid.org/0000-0003-2949-6874"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dejing Dou","raw_affiliation_strings":["University of Oregon"],"affiliations":[{"raw_affiliation_string":"University of Oregon","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103270436","display_name":"Ruoming Jin","orcid":"https://orcid.org/0000-0003-1895-4243"},"institutions":[{"id":"https://openalex.org/I149910238","display_name":"Kent State University","ror":"https://ror.org/049pfb863","country_code":"US","type":"education","lineage":["https://openalex.org/I149910238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruoming Jin","raw_affiliation_strings":["Kent State University"],"affiliations":[{"raw_affiliation_string":"Kent State University","institution_ids":["https://openalex.org/I149910238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100614748","display_name":"Pengwei Wang","orcid":"https://orcid.org/0000-0002-5667-3488"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pengwei Wang","raw_affiliation_strings":["Alibaba"],"affiliations":[{"raw_affiliation_string":"Alibaba","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5044123636"],"corresponding_institution_ids":["https://openalex.org/I82497590"],"apc_list":null,"apc_paid":null,"fwci":1.2601,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.85671525,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"9","issue":null,"first_page":"1018","last_page":"1027"},"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":0.9991000294685364,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9830999970436096,"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/embedding","display_name":"Embedding","score":0.7652648687362671},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.7490314245223999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4716341197490692},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.3912021815776825},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37700897455215454},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37363409996032715},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32780489325523376},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.2940979599952698}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7652648687362671},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.7490314245223999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4716341197490692},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.3912021815776825},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37700897455215454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37363409996032715},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32780489325523376},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.2940979599952698}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006299","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006299","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5799999833106995,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":87,"referenced_works":["https://openalex.org/W1497163089","https://openalex.org/W1587629899","https://openalex.org/W1630959083","https://openalex.org/W1888005072","https://openalex.org/W1908728294","https://openalex.org/W1965970389","https://openalex.org/W1979602484","https://openalex.org/W2004109568","https://openalex.org/W2010187764","https://openalex.org/W2011196043","https://openalex.org/W2025023381","https://openalex.org/W2032192707","https://openalex.org/W2037933327","https://openalex.org/W2073666908","https://openalex.org/W2087905099","https://openalex.org/W2096675082","https://openalex.org/W2097308346","https://openalex.org/W2099970131","https://openalex.org/W2102848467","https://openalex.org/W2105543219","https://openalex.org/W2105981469","https://openalex.org/W2113586398","https://openalex.org/W2118585731","https://openalex.org/W2119482228","https://openalex.org/W2127589108","https://openalex.org/W2132368295","https://openalex.org/W2135827982","https://openalex.org/W2148056538","https://openalex.org/W2149288670","https://openalex.org/W2150593711","https://openalex.org/W2152755144","https://openalex.org/W2154851992","https://openalex.org/W2163776610","https://openalex.org/W2165476871","https://openalex.org/W2165515835","https://openalex.org/W2165770179","https://openalex.org/W2171590421","https://openalex.org/W2187089797","https://openalex.org/W2244118489","https://openalex.org/W2251117138","https://openalex.org/W2254361149","https://openalex.org/W2261028762","https://openalex.org/W2380769351","https://openalex.org/W2393319904","https://openalex.org/W2405761224","https://openalex.org/W2604942799","https://openalex.org/W2614812929","https://openalex.org/W2739994438","https://openalex.org/W2767849480","https://openalex.org/W2771174321","https://openalex.org/W2788792565","https://openalex.org/W2788919350","https://openalex.org/W2789042518","https://openalex.org/W2801800683","https://openalex.org/W2808867307","https://openalex.org/W2808868472","https://openalex.org/W2809156873","https://openalex.org/W2890058722","https://openalex.org/W2905410270","https://openalex.org/W2907921960","https://openalex.org/W2908009723","https://openalex.org/W2914681971","https://openalex.org/W2914833637","https://openalex.org/W2925395532","https://openalex.org/W2949858400","https://openalex.org/W2962756421","https://openalex.org/W2964767742","https://openalex.org/W2985312488","https://openalex.org/W3003961212","https://openalex.org/W3103311102","https://openalex.org/W3103995645","https://openalex.org/W3104097132","https://openalex.org/W3104211877","https://openalex.org/W4232932184","https://openalex.org/W4244478947","https://openalex.org/W4291474301","https://openalex.org/W6636599907","https://openalex.org/W6639917875","https://openalex.org/W6677656871","https://openalex.org/W6682001043","https://openalex.org/W6736262870","https://openalex.org/W6748524405","https://openalex.org/W6749052526","https://openalex.org/W6752380650","https://openalex.org/W6757234447","https://openalex.org/W6769977284","https://openalex.org/W6773048486"],"related_works":["https://openalex.org/W4289743391","https://openalex.org/W2949756879","https://openalex.org/W3043229696","https://openalex.org/W2142909216","https://openalex.org/W3121833847","https://openalex.org/W3121302458","https://openalex.org/W4287375737","https://openalex.org/W4287023929","https://openalex.org/W2890074044","https://openalex.org/W2799012401"],"abstract_inverted_index":{"Network":[0],"embedding":[1,26,33,62,185],"techniques":[2],"aim":[3],"to":[4,51,76,92,120,139,156],"encode":[5],"each":[6,56,83,104,197],"vertex/edge":[7,32,57,61,184],"as":[8],"a":[9,23,110,191],"low-dimensional":[10],"vector,":[11],"enabling":[12],"easy":[13],"integration":[14],"with":[15,34,65,126],"existing":[16],"graph":[17],"mining":[18],"algorithms.":[19],"This":[20],"paper":[21],"presents":[22],"novel":[24],"network":[25,46],"framework,":[27],"VEEMBEDCLASS,":[28],"that":[29,158],"combines":[30],"local":[31,45,60,80,128,183],"deep":[35,86],"matrix":[36,87],"fusion":[37,88],"and":[38,73,96,101,124,131,167,177,186],"Siamese":[39,114,187],"multi-label":[40,115,188],"classification":[41,116,189],"for":[42],"facilitating":[43],"classification-based":[44],"embedding.":[47],"First,":[48],"we":[49,181],"propose":[50],"perform":[52],"the":[53,66,94,140,159],"embeddings":[54],"of":[55,99,105,151,173],"on":[58,82,103],"K":[59,106],"models":[63],"respectively,":[64],"joint":[67],"optimization":[68],"by":[69,163,194],"considering":[70],"both":[71,174],"intra-class":[72],"inter-class":[74],"correlations,":[75],"learn":[77,132],"their":[78,133],"latent":[79],"features":[81],"class.":[84],"The":[85],"technique":[89],"is":[90,118,154],"developed":[91],"preserve":[93],"first-order":[95],"second-order":[97],"proximity":[98],"vertices":[100,123],"edges":[102,125],"classes":[107],"simultaneously.":[108],"Second,":[109],"Student":[111],"t-distribution":[112],"based":[113],"method":[117],"designed":[119],"train":[121],"associated":[122,165],"similar":[127,170],"characteristics":[129],"together":[130],"class":[134,178],"membership":[135],"probabilities,":[136],"in":[137,146,171],"response":[138],"power-law":[141],"vertex":[142],"degree":[143],"distribution":[144],"widespread":[145],"real":[147],"graphs.":[148],"A":[149],"principle":[150],"vertex-edge":[152],"homophily":[153],"introduced":[155],"guarantee":[157],"common":[160],"edge/vertex":[161],"shared":[162],"two":[164],"vertices/edges":[166],"themselves":[168],"are":[169],"terms":[172],"structural":[175],"correlations":[176],"memberships.":[179],"Finally,":[180],"integrate":[182],"into":[190],"unified":[192],"model":[193],"mutually":[195],"enhancing":[196],"other.":[198]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}