{"id":"https://openalex.org/W4390900994","doi":"https://doi.org/10.1145/3635638.3635664","title":"Research on Graph Signal Sampling and Reconstruction Method Based on Causal Emergence","display_name":"Research on Graph Signal Sampling and Reconstruction Method Based on Causal Emergence","publication_year":2023,"publication_date":"2023-10-27","ids":{"openalex":"https://openalex.org/W4390900994","doi":"https://doi.org/10.1145/3635638.3635664"},"language":"en","primary_location":{"id":"doi:10.1145/3635638.3635664","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3635638.3635664","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 6th International Conference on Machine Learning and Machine Intelligence","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/A5028369660","display_name":"Wei-Ping Ren","orcid":"https://orcid.org/0009-0008-4502-5427"},"institutions":[{"id":"https://openalex.org/I16351329","display_name":"China West Normal University","ror":"https://ror.org/04s99y476","country_code":"CN","type":"education","lineage":["https://openalex.org/I16351329"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiping Ren","raw_affiliation_strings":["School of Computing, China West Normal University, China"],"raw_orcid":"https://orcid.org/0009-0008-4502-5427","affiliations":[{"raw_affiliation_string":"School of Computing, China West Normal University, China","institution_ids":["https://openalex.org/I16351329"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077335887","display_name":"Jialin He","orcid":null},"institutions":[{"id":"https://openalex.org/I16351329","display_name":"China West Normal University","ror":"https://ror.org/04s99y476","country_code":"CN","type":"education","lineage":["https://openalex.org/I16351329"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jialin He","raw_affiliation_strings":["School of Computing, China West Normal University, China"],"raw_orcid":"https://orcid.org/0009-0003-8036-9747","affiliations":[{"raw_affiliation_string":"School of Computing, China West Normal University, China","institution_ids":["https://openalex.org/I16351329"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013253716","display_name":"Dong-Xu Cui","orcid":"https://orcid.org/0009-0009-1480-955X"},"institutions":[{"id":"https://openalex.org/I16351329","display_name":"China West Normal University","ror":"https://ror.org/04s99y476","country_code":"CN","type":"education","lineage":["https://openalex.org/I16351329"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongxu Cui","raw_affiliation_strings":["School of Computing, China West Normal University, China"],"raw_orcid":"https://orcid.org/0009-0009-1480-955X","affiliations":[{"raw_affiliation_string":"School of Computing, China West Normal University, China","institution_ids":["https://openalex.org/I16351329"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018483554","display_name":"Xing Fan","orcid":"https://orcid.org/0000-0002-0178-6442"},"institutions":[{"id":"https://openalex.org/I16351329","display_name":"China West Normal University","ror":"https://ror.org/04s99y476","country_code":"CN","type":"education","lineage":["https://openalex.org/I16351329"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Fang","raw_affiliation_strings":["School of Computing, China West Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-0178-6442","affiliations":[{"raw_affiliation_string":"School of Computing, China West Normal University, China","institution_ids":["https://openalex.org/I16351329"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5028369660"],"corresponding_institution_ids":["https://openalex.org/I16351329"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.192435,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"178","last_page":"186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994999766349792,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.8144145011901855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.649476170539856},{"id":"https://openalex.org/keywords/spectral-graph-theory","display_name":"Spectral graph theory","score":0.5573990345001221},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5272252559661865},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5232661962509155},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.4937020242214203},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.436280757188797},{"id":"https://openalex.org/keywords/complex-network","display_name":"Complex network","score":0.42044296860694885},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.415427029132843},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34697574377059937},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.19809934496879578},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.1271473467350006},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.10891485214233398}],"concepts":[{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.8144145011901855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.649476170539856},{"id":"https://openalex.org/C74003402","wikidata":"https://www.wikidata.org/wiki/Q3180727","display_name":"Spectral graph theory","level":5,"score":0.5573990345001221},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5272252559661865},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5232661962509155},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.4937020242214203},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.436280757188797},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.42044296860694885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.415427029132843},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34697574377059937},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.19809934496879578},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.1271473467350006},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.10891485214233398},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3635638.3635664","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3635638.3635664","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 6th International Conference on Machine Learning and Machine Intelligence","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":13,"referenced_works":["https://openalex.org/W1698699930","https://openalex.org/W1965390558","https://openalex.org/W1990488619","https://openalex.org/W2024457004","https://openalex.org/W2101491865","https://openalex.org/W2158787690","https://openalex.org/W2259160646","https://openalex.org/W2332845607","https://openalex.org/W2563342928","https://openalex.org/W2607169934","https://openalex.org/W3002680333","https://openalex.org/W3010884296","https://openalex.org/W4206194712"],"related_works":["https://openalex.org/W2557541666","https://openalex.org/W4386468270","https://openalex.org/W4386468337","https://openalex.org/W4386471465","https://openalex.org/W2346372059","https://openalex.org/W1595344052","https://openalex.org/W2614668157","https://openalex.org/W2030274157","https://openalex.org/W1533309322","https://openalex.org/W2549624392"],"abstract_inverted_index":{"Graph":[0],"signal":[1,76,85,95,203,217,265,307,327],"sampling":[2,148,218,308],"and":[3,14,43,56,93,149,166,183,219,243,309,316],"reconstruction":[4,150,220,280],"techniques":[5],"effectively":[6],"reduce":[7,180],"data":[8,23,34,48],"dimensions":[9,24,49],"while":[10],"preserving":[11],"the":[12,30,38,53,64,72,89,99,112,122,145,152,161,167,185,193,201,208,240,255,273,284,289,299,319,323],"information":[13,126,135,187],"structure":[15,114,165],"contained":[16],"in":[17,28,47],"networks.":[18],"The":[19,176,226],"distortion-free":[20],"reduction":[21,46],"of":[22,33,40,58,66,74,80,111,115,147,154,188,254,272,305,322,325],"not":[25,120],"only":[26],"aids":[27],"reducing":[29],"computational":[31],"complexity":[32],"but":[35],"also":[36],"facilitates":[37],"extraction":[39],"network":[41,60,68,137,156,181,195,242],"features":[42],"attributes.":[44],"This":[45,158,258,311],"provides":[50],"convenience":[51],"for":[52],"better":[54],"analysis":[55,69],"processing":[57,77,86,96],"complex":[59,67],"data,":[61],"ultimately":[62],"enhancing":[63],"efficiency":[65],"tasks.":[70],"Currently,":[71],"field":[73,324],"graph":[75,84,90,94,116,202,209,216,264,286,306,326],"primarily":[78],"consists":[79],"two":[81,106],"fundamental":[82],"frameworks:":[83],"based":[87,97,206,222,234],"on":[88,98,207,223,235,293],"Laplacian":[91,210],"matrix":[92,102],"graph's":[100],"adjacency":[101],".":[103],"Although":[104],"these":[105],"frameworks":[107],"make":[108,192],"full":[109],"use":[110],"topological":[113,164],"models,":[117],"they":[118,131,141],"do":[119,130],"consider":[121],"relationships":[123,169],"between":[124,170],"node":[125,134,252],"within":[127],"networks,":[128],"nor":[129],"integrate":[132],"individual":[133,189],"with":[136],"structural":[138],"information.":[139,157,199],"Furthermore,":[140],"fail":[142],"to":[143,179,191,238,250,262,282,318],"approach":[144,259],"problem":[146],"from":[151],"perspective":[153],"overall":[155],"paper":[159,213],"takes":[160],"network's":[162],"intrinsic":[163],"causal":[168,224,231],"nodes":[171,190],"as":[172],"its":[173],"starting":[174],"point.":[175],"goal":[177],"is":[178],"size":[182],"increase":[184],"effective":[186],"entire":[194],"contain":[196],"richer,":[197],"valuable":[198,314],"Combining":[200],"research":[204,312],"method":[205],"matrix,":[211],"this":[212],"introduces":[214],"a":[215,230,279],"algorithm":[221,227,233,301],"emergence.":[225],"initially":[228],"employs":[229],"emergence":[232],"spectral":[236],"methods":[237,317],"obtain":[239,263],"sampled":[241,256],"subsequently":[244],"utilizes":[245],"classical":[246],"representation":[247],"learning":[248],"algorithms":[249],"acquire":[251],"representations":[253,266],"network.":[257,275],"enables":[260],"us":[261],"that":[267,298],"are":[268],"richer":[269],"than":[270],"those":[271],"original":[274],"Subsequently,":[276],"we":[277],"apply":[278],"framework":[281],"reconstruct":[283],"obtained":[285],"signals.":[287],"Finally,":[288],"experimental":[290],"results":[291],"conducted":[292],"three":[294],"real":[295],"networks":[296],"demonstrate":[297],"proposed":[300],"outperforms":[302],"existing":[303],"theories":[304],"reconstruction.":[310],"contributes":[313],"insights":[315],"further":[320],"development":[321],"processing.":[328]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}