{"id":"https://openalex.org/W3007141922","doi":"https://doi.org/10.1109/bigdata47090.2019.9006488","title":"LATTE: Application Oriented Social Network Embedding","display_name":"LATTE: Application Oriented Social Network Embedding","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007141922","doi":"https://doi.org/10.1109/bigdata47090.2019.9006488","mag":"3007141922"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006488","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/A5076579498","display_name":"Lin Meng","orcid":"https://orcid.org/0000-0003-4351-6923"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lin Meng","raw_affiliation_strings":["IFM Lab, Florida State University, FL, USA"],"affiliations":[{"raw_affiliation_string":"IFM Lab, Florida State University, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103116134","display_name":"Jiyang Bai","orcid":"https://orcid.org/0000-0002-0621-8815"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiyang Bai","raw_affiliation_strings":["IFM Lab, Florida State University, FL, USA"],"affiliations":[{"raw_affiliation_string":"IFM Lab, Florida State University, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100462824","display_name":"Jiawei Zhang","orcid":"https://orcid.org/0000-0002-2111-7617"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiawei Zhang","raw_affiliation_strings":["IFM Lab, Florida State University, FL, USA"],"affiliations":[{"raw_affiliation_string":"IFM Lab, Florida State University, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5076579498"],"corresponding_institution_ids":["https://openalex.org/I103163165"],"apc_list":null,"apc_paid":null,"fwci":0.42,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72739642,"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":"1169","last_page":"1174"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.6141282320022583},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5663949251174927},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.13157543540000916}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6141282320022583},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5663949251174927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.13157543540000916}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006488","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W143174683","https://openalex.org/W1888005072","https://openalex.org/W1987971958","https://openalex.org/W2047532797","https://openalex.org/W2051224630","https://openalex.org/W2062797058","https://openalex.org/W2095293504","https://openalex.org/W2095763169","https://openalex.org/W2101324110","https://openalex.org/W2108502868","https://openalex.org/W2110798204","https://openalex.org/W2121947440","https://openalex.org/W2127795553","https://openalex.org/W2132914434","https://openalex.org/W2143384329","https://openalex.org/W2152430833","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2176433868","https://openalex.org/W2184957013","https://openalex.org/W2294414050","https://openalex.org/W2577283662","https://openalex.org/W2803715695","https://openalex.org/W2908461307","https://openalex.org/W2962756421","https://openalex.org/W2963758104","https://openalex.org/W2964767742","https://openalex.org/W3098742898","https://openalex.org/W3104097132","https://openalex.org/W4294170691","https://openalex.org/W6662241857","https://openalex.org/W6675134712","https://openalex.org/W6676481782","https://openalex.org/W6677945368","https://openalex.org/W6678830454","https://openalex.org/W6682691769","https://openalex.org/W6685357932","https://openalex.org/W6751660716"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2081900870","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032"],"abstract_inverted_index":{"In":[0,66,123],"recent":[1],"years,":[2],"many":[3],"research":[4],"works":[5],"propose":[6,70,111],"to":[7,30,71,132,165],"embed":[8],"the":[9,31,34,40,73,84,88,92,97,103,108,125,134,142,155,162,166,175,203,208,221],"network":[10,61,78,89,114,120,127,146,156,168,178,193],"structured":[11],"data":[12],"into":[13],"a":[14,25,112],"low-dimensional":[15],"feature":[16,26,158],"space,":[17],"where":[18],"each":[19],"node":[20,135],"will":[21],"be":[22,50,130],"represented":[23],"as":[24,198],"vector.":[27],"However,":[28],"due":[29],"detachment":[32],"of":[33,99,177,211,223],"embedding":[35,42,47,79,115,179],"process":[36],"with":[37,55,195],"external":[38,100,181],"tasks,":[39],"learned":[41],"results":[43,205,214],"by":[44,160],"most":[45],"existing":[46,85],"models":[48],"can":[49,129,171],"ineffective":[51],"for":[52],"application":[53,74,182],"tasks":[54,217],"specific":[56],"objectives,":[57],"e.g.,":[58],"community":[59,196],"detection,":[60],"alignment":[62],"or":[63],"information":[64],"diffusion.":[65],"this":[67,224],"paper,":[68],"we":[69,110],"study":[72],"oriented":[75,119],"heterogeneous":[76,126,167,191],"social":[77,192],"problem.":[80],"Significantly":[81],"different":[82],"from":[83],"works,":[86],"besides":[87],"structure":[90,128],"preservation,":[91],"problem":[93],"should":[94],"also":[95,172],"incorporate":[96],"objectives":[98,176],"applications":[101],"in":[102,220],"objective":[104],"function.":[105],"To":[106],"resolve":[107],"problem,":[109],"novel":[113],"framework,":[116],"namely":[117],"\u201capplication":[118],"Embedding\u201d":[121],"(LATTE).":[122],"LATTE,":[124],"applied":[131],"compute":[133],"\u201cdiffusive":[136],"proximity\u201d":[137],"scores,":[138,152],"which":[139,170],"capture":[140],"both":[141],"local":[143],"and":[144,180,202],"global":[145],"structures.":[147],"Based":[148],"on":[149,189,215],"these":[150],"computed":[151],"LATTE":[153],"learns":[154],"representation":[157],"vectors":[159],"extending":[161],"autoencoder":[163],"model":[164],"scenario,":[169],"effectively":[173],"unite":[174],"tasks.":[183],"Extensive":[184],"experiments":[185],"have":[186,206],"been":[187],"done":[188],"real-world":[190],"datasets":[194],"detection":[197],"an":[199],"example":[200],"task,":[201],"experimental":[204],"demonstrated":[207],"outstanding":[209],"performance":[210],"LATTE.":[212],"Experiental":[213],"other":[216],"are":[218],"provided":[219],"full-version":[222],"paper":[225],"at":[226],"[16].":[227]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}