{"id":"https://openalex.org/W4411550973","doi":"https://doi.org/10.1109/tnse.2025.3582501","title":"Efficient Link Prediction in Continuous-Time Dynamic Networks Using Optimal Transmission and Metropolis Hastings Sampling","display_name":"Efficient Link Prediction in Continuous-Time Dynamic Networks Using Optimal Transmission and Metropolis Hastings Sampling","publication_year":2025,"publication_date":"2025-06-23","ids":{"openalex":"https://openalex.org/W4411550973","doi":"https://doi.org/10.1109/tnse.2025.3582501"},"language":"en","primary_location":{"id":"doi:10.1109/tnse.2025.3582501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3582501","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network Science and Engineering","raw_type":"journal-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/A5101944948","display_name":"Ruizhi Zhang","orcid":"https://orcid.org/0000-0002-1317-0063"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruizhi Zhang","raw_affiliation_strings":["School of Mathematical Sciences, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100323831","display_name":"Wei Wei","orcid":"https://orcid.org/0000-0003-3747-9484"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wei","raw_affiliation_strings":["School of Mathematical Sciences, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100650556","display_name":"Qiming Yang","orcid":"https://orcid.org/0009-0002-5709-6798"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiming Yang","raw_affiliation_strings":["School of Mathematical Sciences, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101684813","display_name":"Zhenyu Shi","orcid":"https://orcid.org/0000-0002-8342-8904"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Shi","raw_affiliation_strings":["School of Mathematical Sciences, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101799673","display_name":"Xiangnan Feng","orcid":"https://orcid.org/0000-0003-2824-9329"},"institutions":[{"id":"https://openalex.org/I4210112164","display_name":"Complexity Science Hub Vienna","ror":"https://ror.org/023dz9m50","country_code":"AT","type":"nonprofit","lineage":["https://openalex.org/I4210112164"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Xiangnan Feng","raw_affiliation_strings":["Complexity Science Hub, Vienna, Austria"],"affiliations":[{"raw_affiliation_string":"Complexity Science Hub, Vienna, Austria","institution_ids":["https://openalex.org/I4210112164"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112478420","display_name":"Zhiming Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiming Zheng","raw_affiliation_strings":["Institute of Artificial Intelligence, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101944948"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":1.5567,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85119215,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"194","last_page":"207"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9818000197410583,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.606644332408905},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.549646258354187},{"id":"https://openalex.org/keywords/metropolis\u2013hastings-algorithm","display_name":"Metropolis\u2013Hastings algorithm","score":0.5448167324066162},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.47740283608436584},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3305889964103699},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.32586905360221863},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2492525279521942},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23722386360168457},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.20198583602905273},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.19045937061309814},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.163517564535141}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.606644332408905},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.549646258354187},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.5448167324066162},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.47740283608436584},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3305889964103699},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32586905360221863},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2492525279521942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23722386360168457},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.20198583602905273},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.19045937061309814},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.163517564535141},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tnse.2025.3582501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3582501","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W1564354600","https://openalex.org/W1572272766","https://openalex.org/W1585160083","https://openalex.org/W1888005072","https://openalex.org/W1977382765","https://openalex.org/W1979104937","https://openalex.org/W2020043601","https://openalex.org/W2026417691","https://openalex.org/W2036785686","https://openalex.org/W2046011778","https://openalex.org/W2058077802","https://openalex.org/W2081166868","https://openalex.org/W2108207895","https://openalex.org/W2143668817","https://openalex.org/W2148606196","https://openalex.org/W2153624566","https://openalex.org/W2154851992","https://openalex.org/W2315729860","https://openalex.org/W2420733993","https://openalex.org/W2611395389","https://openalex.org/W2736566518","https://openalex.org/W2798918712","https://openalex.org/W2894402026","https://openalex.org/W2896253905","https://openalex.org/W2937899722","https://openalex.org/W2952461437","https://openalex.org/W2955975072","https://openalex.org/W2962756421","https://openalex.org/W2972205206","https://openalex.org/W2997410851","https://openalex.org/W2997546679","https://openalex.org/W2998116985","https://openalex.org/W3012664577","https://openalex.org/W3016757214","https://openalex.org/W3175459176","https://openalex.org/W3188315507","https://openalex.org/W3194502798","https://openalex.org/W3204508881","https://openalex.org/W3215033462","https://openalex.org/W4200598360","https://openalex.org/W4205269386","https://openalex.org/W4220779330","https://openalex.org/W4225363026","https://openalex.org/W4281871631","https://openalex.org/W4301600272","https://openalex.org/W4360993647","https://openalex.org/W4361284049"],"related_works":["https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W4409439182","https://openalex.org/W2010982255","https://openalex.org/W2042221696","https://openalex.org/W1563670099","https://openalex.org/W2170377829","https://openalex.org/W2053286651","https://openalex.org/W1607416738","https://openalex.org/W2181743346"],"abstract_inverted_index":{"Effective":[0],"link":[1,28,39],"prediction":[2,29],"in":[3,18,96],"continuous":[4],"time":[5],"dynamic":[6,26,100],"networks":[7,101],"is":[8,30],"a":[9,94],"challenging":[10],"issue":[11],"that":[12,63],"has":[13],"received":[14],"much":[15],"research":[16],"attention":[17],"recent":[19],"years.":[20],"A":[21],"widely":[22],"used":[23],"method":[24,57],"for":[25,70],"network":[27],"to":[31,51,67,121,136],"extract":[32],"the":[33,37,44,75,123,127,131,138,155,160,171,175,195],"local":[34,161],"structure":[35,162],"of":[36,140,163,177,197],"target":[38,164],"through":[40],"random":[41],"walks":[42],"on":[43,103,188],"network,":[45],"and":[46,79,85,108,130,166,202],"then":[47],"employ":[48],"encoder":[49,182],"models":[50],"learn":[52],"node":[53,129,144],"features.":[54],"However,":[55],"this":[56,90],"often":[58],"incorporates":[59],"prior":[60],"information,":[61],"assuming":[62],"candidate":[64,133],"neighbors":[65],"adhere":[66],"predefined":[68],"criteria":[69],"spatially":[71],"adjacent,":[72],"without":[73],"considering":[74],"temporally":[76],"proximate":[77],"principles":[78],"its":[80],"consistence":[81],"with":[82,200],"spatial":[83,150,168],"similarity":[84,125],"high-order":[86],"correlation.":[87],"To":[88],"address":[89],"limitation,":[91],"we":[92,116,148],"propose":[93],"framework":[95],"<underline":[97,104,109],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[98,105,110],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">C</u>ontinuous-time":[99],"based":[102],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">O</u>ptimal":[106],"Transmission":[107],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">M</u>etropolis":[111],"Hastings":[112],"sampling":[113],"(COM).":[114],"Specifically,":[115],"utilize":[117],"optimal":[118],"transmission":[119],"theory":[120],"calculate":[122],"distribution":[124],"between":[126],"current":[128],"time-valid":[132],"neighbors,":[134],"aiming":[135],"minimize":[137],"loss":[139],"temporal":[141],"information":[142,145,152,179],"during":[143],"propagation.":[146],"Then":[147],"couple":[149],"structural":[151],"by":[153],"applying":[154],"Metropolis-Hastings":[156],"algorithm,":[157],"which":[158],"captures":[159],"links":[165],"global":[167],"correlations":[169],"within":[170],"network.":[172],"We":[173],"realize":[174],"fusion":[176],"spatiotemporal":[178],"using":[180],"an":[181],"model.":[183],"The":[184],"extensive":[185],"experiments":[186],"performed":[187],"14":[189],"datasets":[190],"from":[191],"different":[192],"fields":[193],"demonstrate":[194],"superiority":[196],"COM":[198],"compared":[199],"representative":[201],"state-of-the-art":[203],"competitors.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-23T05:10:03.516525","created_date":"2025-10-10T00:00:00"}