{"id":"https://openalex.org/W2978013889","doi":"https://doi.org/10.1109/ijcnn.2019.8851979","title":"A Novel Neural Approach for News Reprint Prediction","display_name":"A Novel Neural Approach for News Reprint Prediction","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978013889","doi":"https://doi.org/10.1109/ijcnn.2019.8851979","mag":"2978013889"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5010490481","display_name":"Riheng Yao","orcid":"https://orcid.org/0000-0003-3619-1016"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Riheng Yao","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024206808","display_name":"Qiudan Li","orcid":"https://orcid.org/0000-0002-8714-4562"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiudan Li","raw_affiliation_strings":["The State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100436099","display_name":"Lei Wang","orcid":"https://orcid.org/0000-0003-1810-3019"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei Wang","raw_affiliation_strings":["Beijing Wenge Technology Co., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Wenge Technology Co., Ltd., Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038521974","display_name":"Daniel Zeng","orcid":"https://orcid.org/0000-0002-9046-222X"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daniel Dajun Zeng","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010490481"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.6783,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78541066,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9984999895095825,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9970999956130981,"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":0.9954000115394592,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reprint","display_name":"Reprint","score":0.9919750690460205},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6748152375221252},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4771139919757843},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4665412902832031},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3842291533946991},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.36687102913856506},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35993653535842896}],"concepts":[{"id":"https://openalex.org/C2778489119","wikidata":"https://www.wikidata.org/wiki/Q1962297","display_name":"Reprint","level":2,"score":0.9919750690460205},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6748152375221252},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4771139919757843},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4665412902832031},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3842291533946991},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.36687102913856506},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35993653535842896},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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":48,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1591825359","https://openalex.org/W1775434803","https://openalex.org/W1801721664","https://openalex.org/W1832693441","https://openalex.org/W1854214752","https://openalex.org/W1888005072","https://openalex.org/W1904365287","https://openalex.org/W2064675550","https://openalex.org/W2133564696","https://openalex.org/W2152790380","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2234617458","https://openalex.org/W2242161203","https://openalex.org/W2470673105","https://openalex.org/W2519887557","https://openalex.org/W2607500032","https://openalex.org/W2623187518","https://openalex.org/W2734330123","https://openalex.org/W2740934577","https://openalex.org/W2788614083","https://openalex.org/W2807463780","https://openalex.org/W2900453425","https://openalex.org/W2952792693","https://openalex.org/W2962756421","https://openalex.org/W2963355447","https://openalex.org/W2963502184","https://openalex.org/W2963512530","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W2964308564","https://openalex.org/W3098702884","https://openalex.org/W3103254545","https://openalex.org/W3104097132","https://openalex.org/W4294170691","https://openalex.org/W6631190155","https://openalex.org/W6635189695","https://openalex.org/W6637845829","https://openalex.org/W6639055396","https://openalex.org/W6640036494","https://openalex.org/W6679434410","https://openalex.org/W6682691769","https://openalex.org/W6682948231","https://openalex.org/W6690230747","https://openalex.org/W6726873649","https://openalex.org/W6741265562","https://openalex.org/W6752905549"],"related_works":["https://openalex.org/W2915345684","https://openalex.org/W4298915316","https://openalex.org/W2413040307","https://openalex.org/W4233673714","https://openalex.org/W4247808890","https://openalex.org/W4245319098","https://openalex.org/W2587631327","https://openalex.org/W2476897292","https://openalex.org/W2796348533","https://openalex.org/W2155023359"],"abstract_inverted_index":{"News":[0],"media":[1],"has":[2,62],"become":[3],"a":[4,36,41,101,133,141,178,186,198,233],"prevalent":[5],"information":[6,98,214],"spreading":[7],"platform,":[8],"where":[9],"news":[10,14,24,37,49,58,72,125,149,170,181,212],"sites":[11,70,146,153,168],"can":[12],"reprint":[13,31,40,50,67,75,104,122,130,209,225],"from":[15,77],"other":[16],"sites.":[17,81],"To":[18],"better":[19],"understand":[20],"the":[21,55,78,129,191,194,208],"mechanism":[22],"of":[23,43,57,80,84,97,180,193,224],"propagation,":[25],"it":[26,139],"is":[27],"necessary":[28],"to":[29,91,99,144,160,175],"model":[30,118,196],"behavior":[32,105],"and":[33,71,124,137,169,211,239],"predict":[34,176],"whether":[35,177],"site":[38],"will":[39,182],"piece":[42,179],"news.":[44],"Most":[45],"existing":[46],"works":[47],"in":[48,89],"analysis":[51,223],"focus":[52],"on":[53,65,197],"analyzing":[54],"semantic":[56],"content,":[59],"little":[60],"work":[61],"been":[63],"done":[64],"integrating":[66],"relationship":[68,123,210],"among":[69],"content":[73,150,165,213],"for":[74,236],"prediction":[76,86],"perspective":[79],"The":[82,227],"challenge":[83],"improving":[85],"performance":[87,192],"lies":[88],"how":[90],"effectively":[92],"incorporate":[93],"these":[94],"two":[95],"kinds":[96],"learn":[100,145],"more":[102,163,221],"comprehensive":[103],"model.":[106],"In":[107],"this":[108],"paper,":[109],"we":[110],"propose":[111],"an":[112],"Integrated":[113],"Neural":[114],"Reprint":[115],"Prediction":[116],"(INRP)":[117],"that":[119,205],"considers":[120],"both":[121,207,237],"content.":[126],"It":[127],"models":[128],"relationships":[131],"as":[132,157,232],"directed":[134],"weighted":[135],"graph":[136],"maps":[138],"into":[140,215],"latent":[142],"space":[143],"representations.":[147,166],"During":[148],"modeling":[151],"process,":[152],"representations":[154,171],"are":[155,172],"embedded":[156],"attention":[158],"guidance":[159],"build":[161],"up":[162],"site-specific":[164],"Finally,":[167],"jointly":[173],"modeled":[174],"be":[183],"reprinted":[184],"by":[185],"site.":[187],"We":[188],"empirically":[189],"evaluate":[190],"proposed":[195],"real":[199],"world":[200],"dataset.":[201],"Experimental":[202],"results":[203],"show":[204],"taking":[206],"consideration":[216],"could":[217,230],"allow":[218],"us":[219],"make":[220],"accurate":[222],"patterns.":[226],"mined":[228],"patterns":[229],"serve":[231],"feedback":[234],"channel":[235],"corporations":[238],"management":[240],"departments.":[241]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
