{"id":"https://openalex.org/W7156213666","doi":"https://doi.org/10.1145/3774904.3792232","title":"Social Event Prediction via Fourier Graph Learning","display_name":"Social Event Prediction via Fourier Graph Learning","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7156213666","doi":"https://doi.org/10.1145/3774904.3792232"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792232","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792232","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792232","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051176417","display_name":"Mingjie Qiu","orcid":"https://orcid.org/0009-0007-8855-2879"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingjie Qiu","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0007-8855-2879","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026721130","display_name":"Zhiyi Tan","orcid":"https://orcid.org/0000-0002-1209-2817"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyi Tan","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1209-2817","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007962086","display_name":"Bing\u2010Kun Bao","orcid":"https://orcid.org/0000-0001-5956-831X"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing-Kun Bao","raw_affiliation_strings":["Hefei University of Technology, Hefei, China and Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-5956-831X","affiliations":[{"raw_affiliation_string":"Hefei University of Technology, Hefei, China and Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051176417"],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.96333161,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4587","last_page":"4598"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.7279999852180481,"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":0.7279999852180481,"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":0.03720000013709068,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0357000008225441,"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/interpretability","display_name":"Interpretability","score":0.8166999816894531},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5403000116348267},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5234000086784363},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5166000127792358},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4311999976634979},{"id":"https://openalex.org/keywords/complex-event-processing","display_name":"Complex event processing","score":0.39239999651908875},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.3822000026702881},{"id":"https://openalex.org/keywords/sketch","display_name":"Sketch","score":0.3188000023365021}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8166999816894531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7351999878883362},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.545199990272522},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5343000292778015},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5166000127792358},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4311999976634979},{"id":"https://openalex.org/C123606473","wikidata":"https://www.wikidata.org/wiki/Q907918","display_name":"Complex event processing","level":3,"score":0.39239999651908875},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3822000026702881},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3262999951839447},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.296099990606308},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2793000042438507},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2741999924182892},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2603999972343445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792232","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792232","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792232","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792232","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.44211044907569885,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W82225653","https://openalex.org/W304292935","https://openalex.org/W1525361479","https://openalex.org/W2140948436","https://openalex.org/W2604314403","https://openalex.org/W2728059831","https://openalex.org/W2744043447","https://openalex.org/W2809922372","https://openalex.org/W2950393809","https://openalex.org/W3097986917","https://openalex.org/W3164101172","https://openalex.org/W3182741322","https://openalex.org/W3211666987","https://openalex.org/W4221126228","https://openalex.org/W4307724846","https://openalex.org/W4385562558","https://openalex.org/W4385573535","https://openalex.org/W4387623815","https://openalex.org/W4388755528","https://openalex.org/W4392911065","https://openalex.org/W4396722687","https://openalex.org/W4398181022","https://openalex.org/W4400071817","https://openalex.org/W4401863397","https://openalex.org/W4403780496","https://openalex.org/W4408697844","https://openalex.org/W7131779435"],"related_works":[],"abstract_inverted_index":{"Social":[0],"event":[1,15,63,170,185],"prediction":[2],"has":[3],"garnered":[4],"increasing":[5],"attention":[6],"in":[7],"web-centered":[8],"society.":[9],"Most":[10],"existing":[11],"studies":[12],"represent":[13],"web-based":[14],"stream":[16],"as":[17,133],"chronological":[18],"graph":[19],"sequences,":[20],"then":[21],"leverage":[22],"RNNs":[23,39],"and":[24,29,104,114,141,176,198],"GNNs":[25,55],"to":[26,41,135,182],"model":[27],"temporal":[28,44,119,122,177],"relational":[30],"patterns.":[31,120],"However,":[32],"this":[33,71],"paradigm":[34,77],"is":[35],"inherently":[36],"flawed:":[37],"(1)":[38],"struggle":[40],"capture":[42],"long-term":[43,139],"dependencies,":[45],"ignoring":[46],"those":[47],"temporally":[48],"distant":[49],"but":[50],"influential":[51],"events.":[52],"(2)":[53],"Spatio-temporal":[54],"exhibit":[56],"high":[57],"computational":[58],"complexity":[59,197],"on":[60,145],"large-scale":[61],"real-time":[62],"streams,":[64],"which":[65,130,172],"hinders":[66],"their":[67],"web":[68],"applications.":[69],"To":[70],"end,":[72],"we":[73,89,147],"explore":[74],"a":[75,92,168],"novel":[76,93],"called":[78,96],"Fourier":[79,97,150],"Graph":[80,98,151],"Learning":[81],"from":[82,179],"the":[83],"perspective":[84],"of":[85],"frequency":[86,128],"domain.":[87],"Specifically,":[88],"first":[90],"define":[91],"data":[94],"structure":[95],"(FG).":[99],"In":[100],"FG,":[101,146],"both":[102,138],"nodes":[103],"edges":[105],"are":[106,124],"complex":[107,180],"vectors,":[108],"with":[109,159,193,202],"real":[110],"part":[111,116],"encoding":[112],"semantics":[113,132,175],"imaginary":[115],"representing":[117],"semantic-specific":[118],"These":[121],"patterns":[123,178],"obtained":[125],"by":[126],"semantic-aware":[127],"filter,":[129],"utilizes":[131],"guidance":[134],"adaptively":[136],"incorporates":[137],"dependency":[140],"short-term":[142],"dynamic.":[143],"Based":[144],"further":[148],"propose":[149],"Neural":[152],"Network":[153],"(FGNN).":[154],"It":[155],"replaces":[156],"time-domain":[157],"convolution":[158],"frequency-domain":[160],"multiplication":[161],"for":[162],"efficient":[163],"aggregation.":[164],"FGNN":[165],"also":[166],"includes":[167],"complex-valued":[169],"decoder,":[171],"fully":[173],"leverages":[174],"space":[181],"predict":[183],"future":[184],"probabilities.":[186],"Extensive":[187],"experiments":[188],"show":[189],"our":[190],"superior":[191],"performance":[192],"higher":[194],"accuracy,":[195],"less":[196],"better":[199],"interpretability":[200],"compared":[201],"baselines.":[203]},"counts_by_year":[],"updated_date":"2026-04-28T06:12:00.211691","created_date":"2026-04-28T00:00:00"}
