{"id":"https://openalex.org/W4406461040","doi":"https://doi.org/10.1109/bigdata62323.2024.10825046","title":"DyGCL: Dynamic Graph Contrastive Learning For Event Prediction","display_name":"DyGCL: Dynamic Graph Contrastive Learning For Event Prediction","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406461040","doi":"https://doi.org/10.1109/bigdata62323.2024.10825046"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825046","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5101343483","display_name":"Muhammed Ifte Khairul Islam","orcid":null},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Muhammed Ifte Khairul Islam","raw_affiliation_strings":["Georgia State University,Dept of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Dept of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043368108","display_name":"Khaled Mohammed Saifuddin","orcid":"https://orcid.org/0000-0002-0903-937X"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Khaled Mohammed Saifuddin","raw_affiliation_strings":["Northeastern University,Boston,MA,USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University,Boston,MA,USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051902744","display_name":"Tanvir Hossain","orcid":"https://orcid.org/0000-0002-4765-6600"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tanvir Hossain","raw_affiliation_strings":["Georgia State University,Dept of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Dept of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080080406","display_name":"Esra Akba\u015f","orcid":"https://orcid.org/0000-0002-8817-2442"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Esra Akbas","raw_affiliation_strings":["Georgia State University,Dept of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Dept of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101343483"],"corresponding_institution_ids":["https://openalex.org/I181565077"],"apc_list":null,"apc_paid":null,"fwci":2.0604,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.88510256,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"559","last_page":"568"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9929999709129333,"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":0.7359054684638977},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5181049704551697},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.49398380517959595},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4451074004173279},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4170725345611572},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35218459367752075},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2692090570926666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7359054684638977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5181049704551697},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49398380517959595},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4451074004173279},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4170725345611572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35218459367752075},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2692090570926666},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825046","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W144670803","https://openalex.org/W1975594555","https://openalex.org/W1999529874","https://openalex.org/W2147194983","https://openalex.org/W2171468534","https://openalex.org/W2321533354","https://openalex.org/W2531563875","https://openalex.org/W2785325870","https://openalex.org/W2788235048","https://openalex.org/W2901504064","https://openalex.org/W2907101105","https://openalex.org/W2950369002","https://openalex.org/W2998116985","https://openalex.org/W2998313947","https://openalex.org/W3086452730","https://openalex.org/W3093695087","https://openalex.org/W3208451974","https://openalex.org/W4220779330","https://openalex.org/W4283796586","https://openalex.org/W4283818575","https://openalex.org/W4286896158","https://openalex.org/W4290877727","https://openalex.org/W4295312788","https://openalex.org/W4378531128","https://openalex.org/W4382239247","https://openalex.org/W4385282405","https://openalex.org/W6726873649","https://openalex.org/W6732431570","https://openalex.org/W6747899497","https://openalex.org/W6753331806","https://openalex.org/W6760045743","https://openalex.org/W6761665040","https://openalex.org/W6766156693","https://openalex.org/W6766978945","https://openalex.org/W6774314701","https://openalex.org/W6779518175","https://openalex.org/W6803586825","https://openalex.org/W6867907617"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Predicting":[0],"events,":[1],"ranging":[2],"from":[3,41,199],"political":[4],"unrest":[5],"to":[6,37,44,56,162,184,214],"disease":[7],"outbreaks":[8],"and":[9,48,101,128,212],"criminal":[10],"activities,":[11],"stands":[12],"as":[13,28,66,172],"a":[14,29,67,143,158,180],"pivotal":[15],"task":[16],"in":[17,83,138],"proactively":[18],"addressing":[19],"emerging":[20],"challenges.":[21],"Despite":[22],"the":[23,49,72,93,115,125,129,165,173,186,192,196,227],"richness":[24],"of":[25,52,124,131,169,191],"textual":[26,77],"data":[27,78],"source":[30],"for":[31,70,106,150,230],"event":[32,108,151,231],"detection,":[33],"it":[34,178],"is":[35],"challenging":[36],"extract":[38],"contextual":[39],"information":[40],"documents":[42],"due":[43],"their":[45],"complex":[46],"structure":[47,127,168],"dynamic":[50,59,167,188],"evolution":[51],"events.":[53,217],"In":[54],"response":[55],"this":[57,84,139],"challenge,":[58],"Graph":[60,145],"Neural":[61],"Networks":[62],"(GNNs)":[63],"have":[64],"emerged":[65],"promising":[68],"tool":[69],"capturing":[71],"intricate":[73],"patterns":[74],"embedded":[75],"within":[76],"graphs.":[79,133,194],"Nevertheless,":[80],"many":[81],"models":[82],"domain":[85],"primarily":[86],"rely":[87],"on":[88,233],"local":[89,116,159,166],"node-level":[90,100],"representations,":[91],"overlooking":[92],"essential":[94],"global":[95,126,181],"graph-level":[96,102,119],"context.":[97],"However,":[98],"both":[99,200],"representations":[103,111,120,198],"are":[104,206],"critical":[105],"effective":[107],"prediction.":[109,152],"Node-level":[110],"provide":[112],"insight":[113],"into":[114],"structure,":[117],"while":[118],"offer":[121],"an":[122,209],"understanding":[123],"evaluation":[130],"temporal":[132],"To":[134],"address":[135],"these":[136],"challenges,":[137],"paper,":[140],"we":[141],"propose":[142],"Dynamic":[144],"Contrastive":[146],"Learning":[147],"(DyGCL)":[148],"method":[149,225],"Our":[153,218],"model":[154],"DyGCL":[155],"first":[156],"employs":[157],"view":[160,182],"encoder":[161,183],"effectively":[163],"capture":[164],"input":[170,193],"graphs":[171],"evolving":[174],"node":[175],"representations.":[176],"Then,":[177],"performs":[179],"perceive":[185],"hierarchical":[187],"graph":[189,197],"representation":[190],"Finally,":[195],"encoders,":[201],"optimized":[202],"via":[203],"contrastive":[204],"learning,":[205],"combined":[207],"with":[208],"attention":[210],"mechanism":[211],"utilized":[213],"predict":[215],"future":[216],"extensive":[219],"experiments":[220],"demonstrate":[221],"that":[222],"our":[223],"proposed":[224],"outperforms":[226],"state-of-the-art":[228],"methods":[229],"prediction":[232],"six":[234],"real-world":[235],"datasets.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-02-25T23:00:34.991745","created_date":"2025-10-10T00:00:00"}