{"id":"https://openalex.org/W2903036385","doi":"https://doi.org/10.1109/icpr.2018.8545577","title":"Enhanced Network Embedding with Text Information","display_name":"Enhanced Network Embedding with Text Information","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903036385","doi":"https://doi.org/10.1109/icpr.2018.8545577","mag":"2903036385"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545577","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545577","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102831864","display_name":"Shuang Yang","orcid":"https://orcid.org/0000-0002-6814-6239"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210134929","display_name":"Jilin Province Science and Technology Department","ror":"https://ror.org/049x38272","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Yang","raw_affiliation_strings":["Department of Computer Science and Technology, Jilin University, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716","https://openalex.org/I4210134929"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028694889","display_name":"Bo Yang","orcid":"https://orcid.org/0000-0003-1927-8419"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210134929","display_name":"Jilin Province Science and Technology Department","ror":"https://ror.org/049x38272","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Yang","raw_affiliation_strings":["Department of Computer Science and Technology, Jilin University, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716","https://openalex.org/I4210134929"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"326","last_page":"331"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9970999956130981,"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":0.9794999957084656,"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":0.7224982976913452},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6574869155883789},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5358957052230835},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5332657098770142},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4987337589263916},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.48740723729133606},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.459879070520401},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4533495306968689},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4378901720046997},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3687097728252411}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7224982976913452},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6574869155883789},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5358957052230835},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5332657098770142},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4987337589263916},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.48740723729133606},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.459879070520401},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4533495306968689},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4378901720046997},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3687097728252411},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545577","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545577","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W2013029404","https://openalex.org/W2037933327","https://openalex.org/W2090891622","https://openalex.org/W2097027133","https://openalex.org/W2118585731","https://openalex.org/W2119571791","https://openalex.org/W2127048411","https://openalex.org/W2135029798","https://openalex.org/W2150461699","https://openalex.org/W2153959628","https://openalex.org/W2154851992","https://openalex.org/W2168627253","https://openalex.org/W2242161203","https://openalex.org/W2393319904","https://openalex.org/W2411855254","https://openalex.org/W2583803680","https://openalex.org/W2604942799","https://openalex.org/W2622489478","https://openalex.org/W2739747431","https://openalex.org/W2962756421","https://openalex.org/W3102641634","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W3143596294","https://openalex.org/W4232932184","https://openalex.org/W6677656871","https://openalex.org/W6680012447","https://openalex.org/W6690230747","https://openalex.org/W6715220567","https://openalex.org/W6736262870"],"related_works":["https://openalex.org/W2983744209","https://openalex.org/W2942985259","https://openalex.org/W2026121273","https://openalex.org/W2801772698","https://openalex.org/W66955737","https://openalex.org/W2102106825","https://openalex.org/W2752271443","https://openalex.org/W2912933387","https://openalex.org/W2997669297","https://openalex.org/W2951545510"],"abstract_inverted_index":{"Network":[0,77],"embedding":[1,27,62,112,152],"aims":[2],"at":[3],"learning":[4],"the":[5,32,35,70,82,98,110,119,124,132,148,154,169],"low-dimensional":[6],"and":[7,92,104,115,135],"continuous":[8],"vector":[9],"representation":[10],"for":[11,143],"each":[12],"node":[13,102],"in":[14,19],"networks,":[15],"which":[16,42,130],"is":[17,43,50,63],"useful":[18],"many":[20],"real":[21],"applications.":[22],"While":[23],"most":[24],"existing":[25],"network":[26,33,48,61,90,111,133,151],"methods":[28],"only":[29],"focus":[30],"on":[31,153,163],"structure,":[34,49],"rich":[36],"text":[37,58,93,105,136,144],"information":[38,59,94],"associated":[39],"with":[40,175],"nodes,":[41],"often":[44],"closely":[45],"related":[46],"to":[47,55,88,108],"widely":[51],"neglected.":[52],"Thus,":[53],"how":[54],"effectively":[56],"incorporate":[57],"into":[60],"a":[64,74],"problem":[65],"worth":[66],"studying.":[67],"To":[68],"solve":[69],"problem,":[71],"we":[72],"propose":[73],"Text":[75],"Enhanced":[76],"Embedding":[78],"(TENE)":[79],"method":[80],"under":[81,123],"framework":[83],"of":[84,121,126,150,156,159,172],"non-negative":[85],"matrix":[86,129,139],"factorization":[87],"integrate":[89],"structure":[91,107,134],"together.":[95],"We":[96,146],"explore":[97],"consistent":[99],"relationship":[100],"between":[101],"representations":[103,120],"cluster":[106,137],"make":[109],"more":[113],"informative":[114],"discriminative.":[116],"TENE":[117,173],"learns":[118],"nodes":[122],"guidance":[125],"both":[127],"proximity":[128],"captures":[131],"membership":[138],"derived":[140],"from":[141],"clustering":[142],"information.":[145],"evaluate":[147],"quality":[149],"task":[155],"multi-class":[157],"classification":[158],"nodes.":[160],"Experimental":[161],"results":[162],"all":[164],"three":[165],"real-world":[166],"datasets":[167],"show":[168],"superior":[170],"performance":[171],"compared":[174],"baselines.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}