{"id":"https://openalex.org/W2989826725","doi":"https://doi.org/10.1109/asonam49781.2020.9381423","title":"Multi-task Network Embedding with Adaptive Loss Weighting","display_name":"Multi-task Network Embedding with Adaptive Loss Weighting","publication_year":2020,"publication_date":"2020-12-07","ids":{"openalex":"https://openalex.org/W2989826725","doi":"https://doi.org/10.1109/asonam49781.2020.9381423","mag":"2989826725"},"language":"en","primary_location":{"id":"doi:10.1109/asonam49781.2020.9381423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asonam49781.2020.9381423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","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/A5079198705","display_name":"Fatemeh Salehi Rizi","orcid":null},"institutions":[{"id":"https://openalex.org/I186354981","display_name":"University of Passau","ror":"https://ror.org/05ydjnb78","country_code":"DE","type":"education","lineage":["https://openalex.org/I186354981"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Fatemeh Salehi Rizi","raw_affiliation_strings":["University of Passau, Faculty of Computer Science and Mathematics, Passau, Germany"],"affiliations":[{"raw_affiliation_string":"University of Passau, Faculty of Computer Science and Mathematics, Passau, Germany","institution_ids":["https://openalex.org/I186354981"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006866152","display_name":"Michael Granitzer","orcid":"https://orcid.org/0000-0003-3566-5507"},"institutions":[{"id":"https://openalex.org/I186354981","display_name":"University of Passau","ror":"https://ror.org/05ydjnb78","country_code":"DE","type":"education","lineage":["https://openalex.org/I186354981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Granitzer","raw_affiliation_strings":["University of Passau, Faculty of Computer Science and Mathematics, Passau, Germany"],"affiliations":[{"raw_affiliation_string":"University of Passau, Faculty of Computer Science and Mathematics, Passau, Germany","institution_ids":["https://openalex.org/I186354981"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079198705"],"corresponding_institution_ids":["https://openalex.org/I186354981"],"apc_list":null,"apc_paid":null,"fwci":0.2651,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60652189,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9990000128746033,"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":0.9990000128746033,"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/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/weighting","display_name":"Weighting","score":0.8276273012161255},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8210992813110352},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6969168186187744},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6549332737922668},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6516922116279602},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5557702779769897},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5352424383163452},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5094855427742004},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.508845329284668},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5081214904785156},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4668087959289551},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.44238194823265076},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.43979620933532715}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.8276273012161255},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8210992813110352},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6969168186187744},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6549332737922668},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6516922116279602},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5557702779769897},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5352424383163452},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5094855427742004},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.508845329284668},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5081214904785156},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4668087959289551},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.44238194823265076},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.43979620933532715},{"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asonam49781.2020.9381423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asonam49781.2020.9381423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","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":20,"referenced_works":["https://openalex.org/W2242427765","https://openalex.org/W2410664773","https://openalex.org/W2583803680","https://openalex.org/W2612872092","https://openalex.org/W2617663343","https://openalex.org/W2622489478","https://openalex.org/W2758602106","https://openalex.org/W2769550288","https://openalex.org/W2808000122","https://openalex.org/W2898210895","https://openalex.org/W2908461307","https://openalex.org/W2914080035","https://openalex.org/W2942588996","https://openalex.org/W2962756421","https://openalex.org/W2970298096","https://openalex.org/W2991283396","https://openalex.org/W3103452448","https://openalex.org/W3104005311","https://openalex.org/W4295240482","https://openalex.org/W6770441318"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2180954594","https://openalex.org/W2354198838","https://openalex.org/W1989130879","https://openalex.org/W2052835778","https://openalex.org/W2103419012","https://openalex.org/W2049003611","https://openalex.org/W2108418243","https://openalex.org/W2127804977","https://openalex.org/W164103134"],"abstract_inverted_index":{"Network":[0],"embedding":[1,76],"is":[2,80,97],"to":[3,60,131,158],"learn":[4],"low-dimensional":[5],"representations":[6],"of":[7,28,65,118,123,140,155],"nodes":[8,20],"which":[9,32,58],"mostly":[10],"preserve":[11],"the":[12,84,138,159],"network":[13,55,67],"topological":[14],"structure.":[15],"In":[16],"real-world":[17,151],"networks,":[18],"however,":[19],"are":[21,33],"often":[22],"associated":[23],"with":[24],"a":[25,49,62],"rich":[26],"set":[27],"attributes":[29],"and":[30,71,100],"labels":[31],"potentially":[34],"valuable":[35],"in":[36,107],"seeking":[37],"more":[38],"effective":[39],"vector":[40],"representations.":[41],"To":[42],"properly":[43],"utilize":[44],"this":[45],"information,":[46],"we":[47,110],"propose":[48],"Joint":[50],"Autoencoders":[51],"framework":[52],"for":[53,136],"Multi-task":[54],"Embedding":[56],"(JAME),":[57],"aims":[59],"encode":[61],"shared":[63],"representation":[64],"local":[66],"structure,":[68],"node":[69,73],"attributes,":[70],"available":[72],"labels.":[74],"Jointly":[75],"via":[77],"multi-task":[78,104],"learning":[79,105,119],"strongly":[81],"dependent":[82],"on":[83,144,149],"relative":[85],"weighting":[86,115],"between":[87],"each":[88],"task's":[89],"loss":[90,114,124],"function.":[91],"Tuning":[92],"these":[93],"weights":[94,125,135],"by":[95],"hand":[96],"an":[98,112,120],"expensive":[99],"difficult":[101],"process,":[102],"making":[103],"prohibitive":[106],"practice.":[108],"Therefore,":[109],"define":[111],"adaptive":[113],"layer":[116],"capable":[117],"optimal":[121],"combination":[122],"during":[126],"model":[127],"training.":[128],"This":[129],"allows":[130],"dynamically":[132],"updates":[133],"task":[134,145],"controlling":[137],"influence":[139],"individual":[141],"tasks":[142],"based":[143],"difficulty.":[146],"Empirical":[147],"evaluations":[148],"three":[150],"datasets":[152],"demonstrate":[153],"effectiveness":[154],"JAME":[156],"compared":[157],"relevant":[160],"baseline":[161],"methods.":[162]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}