{"id":"https://openalex.org/W4288080214","doi":"https://doi.org/10.1145/3394486.3403276","title":"Knowing your FATE","display_name":"Knowing your FATE","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W4288080214","doi":"https://doi.org/10.1145/3394486.3403276"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403276","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403276","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403276","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403276","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070663881","display_name":"Xianfeng Tang","orcid":"https://orcid.org/0000-0002-7955-3104"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xianfeng Tang","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016839661","display_name":"Yozen Liu","orcid":"https://orcid.org/0000-0002-2107-504X"},"institutions":[{"id":"https://openalex.org/I4210142583","display_name":"Snap (United States)","ror":"https://ror.org/04dgkhg68","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142583"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yozen Liu","raw_affiliation_strings":["Snap Inc., Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Snap Inc., Los Angeles, CA, USA","institution_ids":["https://openalex.org/I4210142583"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101799872","display_name":"Neil Shah","orcid":"https://orcid.org/0000-0003-3261-8430"},"institutions":[{"id":"https://openalex.org/I4210142583","display_name":"Snap (United States)","ror":"https://ror.org/04dgkhg68","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142583"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neil Shah","raw_affiliation_strings":["Snap Inc., Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Snap Inc., Los Angeles, CA, USA","institution_ids":["https://openalex.org/I4210142583"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101666398","display_name":"Xiaolin Shi","orcid":"https://orcid.org/0000-0002-3705-1552"},"institutions":[{"id":"https://openalex.org/I4210142583","display_name":"Snap (United States)","ror":"https://ror.org/04dgkhg68","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142583"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaolin Shi","raw_affiliation_strings":["Snap Inc., Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Snap Inc., Los Angeles, CA, USA","institution_ids":["https://openalex.org/I4210142583"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038025289","display_name":"Prasenjit Mitra","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prasenjit Mitra","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011048500","display_name":"Suhang Wang","orcid":"https://orcid.org/0000-0003-3448-4878"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suhang Wang","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.5453,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.95930092,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2269","last_page":"2279"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9927999973297119,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9927999973297119,"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":0.9886000156402588,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9883999824523926,"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":0.567952036857605}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.567952036857605}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3394486.3403276","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403276","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403276","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2006.06427","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.06427","pdf_url":"https://arxiv.org/pdf/2006.06427","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3394486.3403276","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403276","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403276","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7366345995","display_name":"III: Small: Collaborative Research: Effective Labeled Data Generation via Generative Adversarial Learning","funder_award_id":"1909702","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4288080214.pdf","grobid_xml":"https://content.openalex.org/works/W4288080214.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1510898630","https://openalex.org/W1868427966","https://openalex.org/W2016738483","https://openalex.org/W2114079787","https://openalex.org/W2121946324","https://openalex.org/W2138906630","https://openalex.org/W2152933328","https://openalex.org/W2158599326","https://openalex.org/W2171033594","https://openalex.org/W2295598076","https://openalex.org/W2339311053","https://openalex.org/W2388272999","https://openalex.org/W2516809705","https://openalex.org/W2528820915","https://openalex.org/W2788040807","https://openalex.org/W2807021761","https://openalex.org/W2809496930","https://openalex.org/W2884555939","https://openalex.org/W2945827377","https://openalex.org/W2950616914","https://openalex.org/W2951256120","https://openalex.org/W2951307134","https://openalex.org/W2962878352","https://openalex.org/W2963066159","https://openalex.org/W2982327501","https://openalex.org/W2994598354","https://openalex.org/W2997705255","https://openalex.org/W3100848837","https://openalex.org/W3101553402","https://openalex.org/W3104667978","https://openalex.org/W3106113697","https://openalex.org/W4301039088"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"With":[0],"the":[1],"rapid":[2],"growth":[3],"and":[4,29,113,132,155,163,184,197],"prevalence":[5],"of":[6,62,77],"social":[7,68],"network":[8,69,129,153],"applications":[9],"(Apps)":[10],"in":[11,51,159],"recent":[12],"years,":[13],"understanding":[14],"user":[15,42,64,78,107,111],"engagement":[16,43,65,79,119],"has":[17],"become":[18],"increasingly":[19],"important,":[20],"to":[21,105,116],"provide":[22],"useful":[23],"insights":[24],"for":[25,40,67,80,139],"future":[26,86],"App":[27],"design":[28,91],"development.":[30],"While":[31],"several":[32],"promising":[33],"neural":[34,94,128],"modeling":[35],"approaches":[36,180],"were":[37],"recently":[38],"pioneered":[39],"accurate":[41],"prediction,":[44],"their":[45],"black-box":[46],"designs":[47],"are":[48],"unfortunately":[49],"limited":[50],"model":[52],"explainability.":[53],"In":[54],"this":[55],"paper,":[56],"we":[57,72,90,103],"study":[58],"a":[59,74,125,133],"novel":[60],"problem":[61],"explainable":[63,118],"prediction":[66,161],"Apps.":[70],"First,":[71],"propose":[73],"flexible":[75],"definition":[76],"various":[81],"business":[82],"scenarios,":[83],"based":[84,123,143],"on":[85,124,144,170],"metric":[87],"expectations.":[88],"Next,":[89],"an":[92],"end-to-end":[93],"framework,":[95],"FATE,":[96,193],"which":[97,137],"incorporates":[98],"three":[99],"key":[100],"factors":[101],"that":[102],"identify":[104],"influence":[106],"engagement,":[108],"namely":[109],"friendships,":[110],"actions,":[112],"temporal":[114],"dynamics":[115],"achieve":[117],"predictions.":[120],"FATE":[121,177],"is":[122],"tensor-based":[126],"graph":[127],"(GNN),":[130],"LSTM":[131],"mixture":[134],"attention":[135],"mechanism,":[136],"allows":[138],"(a)":[140],"predictive":[141],"explanations":[142,191],"learned":[145],"weights":[146],"across":[147],"different":[148],"feature":[149],"categories,":[150],"(b)":[151],"reduced":[152],"complexity,":[154],"(c)":[156],"improved":[157],"performance":[158],"both":[160],"accuracy":[162],"training/inference":[164],"time.":[165],"We":[166,188],"conduct":[167],"extensive":[168],"experiments":[169],"two":[171],"large-scale":[172],"datasets":[173],"from":[174,192],"Snapchat,":[175],"where":[176],"outperforms":[178],"state-of-the-art":[179],"by":[181],"10%":[182],"error":[183],"20%":[185],"runtime":[186],"reduction.":[187],"also":[189],"evaluate":[190],"showing":[194],"strong":[195],"quantitative":[196],"qualitative":[198],"performance.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2022-07-28T00:00:00"}
