{"id":"https://openalex.org/W7156151431","doi":"https://doi.org/10.1145/3774904.3792194","title":"EIAN: Explicit Interaction-aware Attention Network for Interpretable Event Modeling","display_name":"EIAN: Explicit Interaction-aware Attention Network for Interpretable Event Modeling","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7156151431","doi":"https://doi.org/10.1145/3774904.3792194"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792194","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792194","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/10.1145/3774904.3792194","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134723487","display_name":"Jiping Zhang","orcid":"https://orcid.org/0009-0007-2966-7932"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiping Zhang","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0007-2966-7932","affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134716469","display_name":"Hua Zhu","orcid":"https://orcid.org/0009-0005-1696-6605"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hua Zhu","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0005-1696-6605","affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113959149","display_name":"Hong Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Huang","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0000-0002-5282-551X","affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101797428","display_name":"Yi Zhou","orcid":"https://orcid.org/0000-0001-9610-4370"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongkang Zhou","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0008-5757-4554","affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106713405","display_name":"Kehan Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kehan Yin","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0004-9471-6161","affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100691220","display_name":"Bang Liu","orcid":"https://orcid.org/0000-0002-3539-6020"},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Bang Liu","raw_affiliation_strings":["University of Montreal, Montreal, Canada"],"raw_orcid":"https://orcid.org/0000-0002-9483-8984","affiliations":[{"raw_affiliation_string":"University of Montreal, Montreal, Canada","institution_ids":["https://openalex.org/I70931966"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5134723487"],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.96183138,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7001","last_page":"7012"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11830","display_name":"Point processes and geometric inequalities","score":0.34279999136924744,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11830","display_name":"Point processes and geometric inequalities","score":0.34279999136924744,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.093299999833107,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.032099999487400055,"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/event","display_name":"Event (particle physics)","score":0.7434999942779541},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5692999958992004},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5652999877929688},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5573999881744385},{"id":"https://openalex.org/keywords/point-process","display_name":"Point process","score":0.5572999715805054},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.48730000853538513},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.487199991941452},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4575999975204468},{"id":"https://openalex.org/keywords/complex-event-processing","display_name":"Complex event processing","score":0.44029998779296875}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.79339998960495},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.7434999942779541},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5692999958992004},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5652999877929688},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5573999881744385},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.5572999715805054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5069000124931335},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.48730000853538513},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.487199991941452},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4575999975204468},{"id":"https://openalex.org/C123606473","wikidata":"https://www.wikidata.org/wiki/Q907918","display_name":"Complex event processing","level":3,"score":0.44029998779296875},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.42750000953674316},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.3862000107765198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.365200012922287},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29109999537467957},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.288100004196167},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.28130000829696655},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792194","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792194","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:10.1145/3774904.3792194","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792194","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2064758233","https://openalex.org/W2069849731","https://openalex.org/W2122966699","https://openalex.org/W2166851633","https://openalex.org/W2469279958","https://openalex.org/W2509830164","https://openalex.org/W2914034186","https://openalex.org/W2949377321","https://openalex.org/W2964199361","https://openalex.org/W2971196067","https://openalex.org/W3189554159","https://openalex.org/W4385763959","https://openalex.org/W4388979610","https://openalex.org/W4404650820"],"related_works":[],"abstract_inverted_index":{"Event":[0],"sequences":[1],"are":[2,22,127],"integral":[3],"to":[4,133],"domains":[5],"such":[6],"as":[7],"e-commerce,":[8],"social":[9],"networks,":[10],"and":[11,19,60,87,111,154,165],"healthcare.":[12],"Traditional":[13],"point":[14,38],"process":[15],"models,":[16],"like":[17],"Poisson":[18],"Hawkes":[20],"processes,":[21],"foundational":[23],"but":[24,45],"limited":[25],"by":[26,82],"rigid":[27],"parametric":[28],"assumptions,":[29],"constraining":[30],"their":[31],"flexibility":[32,164],"in":[33,138,151],"complex":[34],"real-world":[35],"scenarios.":[36,141],"Neural":[37],"processes":[39],"offer":[40],"a":[41,75,112],"more":[42],"adaptable":[43],"alternative,":[44],"typically":[46],"perform":[47],"implicit":[48],"sequence":[49],"modeling,":[50],"which":[51],"does":[52],"not":[53],"fully":[54],"exploit":[55],"critical":[56],"event":[57,80,89,109,120,159],"interaction":[58,114,131,160],"patterns":[59],"limits":[61],"transparency.":[62],"To":[63],"address":[64],"these":[65],"challenges,":[66],"we":[67],"introduce":[68],"the":[69,103,130],"Explicit":[70],"Interaction-aware":[71],"Attention":[72],"Network":[73],"(EIAN),":[74],"novel":[76],"model":[77],"that":[78,101,116,145],"enhances":[79],"modeling":[81],"explicitly":[83],"capturing":[84],"both":[85,163],"intra-type":[86,98],"cross-type":[88,113],"interactions.":[90],"Specifically,":[91],"EIAN":[92,146],"employs":[93],"two":[94,123],"key":[95],"components:":[96],"an":[97],"temporal":[99,105,124,140],"encoder":[100],"preserves":[102],"unique":[104],"dynamics":[106],"within":[107],"each":[108],"type,":[110],"decoder":[115,132],"highlights":[117],"interactions":[118],"across":[119],"types.":[121],"Furthermore,":[122],"encoding":[125],"mechanisms":[126],"integrated":[128],"into":[129,158],"handle":[134],"irregular":[135],"inter-event":[136],"intervals":[137],"diverse":[139],"Extensive":[142],"experiments":[143],"show":[144],"consistently":[147],"outperforms":[148],"existing":[149],"models":[150],"predictive":[152],"performance":[153],"provides":[155],"deeper":[156],"insights":[157],"patterns,":[161],"advancing":[162],"interpretability.":[166],"Our":[167],"code":[168],"is":[169],"available":[170],"at":[171],"https://github.com/CGCL-codes/EIAN.git.":[172]},"counts_by_year":[],"updated_date":"2026-04-28T06:12:00.211691","created_date":"2026-04-28T00:00:00"}
