{"id":"https://openalex.org/W4417515136","doi":"https://doi.org/10.48550/arxiv.2505.06998","title":"Assessing the Robustness and Reducibility of Multiplex Networks with Embedding-Aided Interlayer Similarities","display_name":"Assessing the Robustness and Reducibility of Multiplex Networks with Embedding-Aided Interlayer Similarities","publication_year":2025,"publication_date":"2025-05-11","ids":{"openalex":"https://openalex.org/W4417515136","doi":"https://doi.org/10.48550/arxiv.2505.06998"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2505.06998","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.06998","pdf_url":"https://arxiv.org/pdf/2505.06998","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2505.06998","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117634904","display_name":"Haoran Nan","orcid":"https://orcid.org/0009-0003-6712-9597"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nan, Haoran","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003569392","display_name":"Senquan Wang","orcid":"https://orcid.org/0009-0009-0566-933X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Senquan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117634905","display_name":"Chun Ouyang","orcid":"https://orcid.org/0009-0007-9518-332X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ouyang, Chun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089201098","display_name":"Yvonne Zhou","orcid":"https://orcid.org/0009-0000-9986-3671"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yanchen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112136659","display_name":"Weiwei Gu","orcid":"https://orcid.org/0009-0002-5177-5730"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Weiwei","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5117634904"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.4325000047683716,"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"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.4325000047683716,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.41200000047683716,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.09749999642372131,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.781499981880188},{"id":"https://openalex.org/keywords/multiplex","display_name":"Multiplex","score":0.6330999732017517},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6118999719619751},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5964999794960022},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5134999752044678},{"id":"https://openalex.org/keywords/structural-similarity","display_name":"Structural similarity","score":0.4000000059604645},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.39320001006126404},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.38100001215934753}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.781499981880188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6470000147819519},{"id":"https://openalex.org/C2781188995","wikidata":"https://www.wikidata.org/wiki/Q6934982","display_name":"Multiplex","level":2,"score":0.6330999732017517},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6118999719619751},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5964999794960022},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5300999879837036},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5134999752044678},{"id":"https://openalex.org/C139489369","wikidata":"https://www.wikidata.org/wiki/Q770846","display_name":"Structural similarity","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3955000042915344},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3894999921321869},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C2988224531","wikidata":"https://www.wikidata.org/wiki/Q20830730","display_name":"Network structure","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3151000142097473},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C119453123","wikidata":"https://www.wikidata.org/wiki/Q262372","display_name":"Self-similarity","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2615000009536743}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2505.06998","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.06998","pdf_url":"https://arxiv.org/pdf/2505.06998","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2505.06998","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2505.06998","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2505.06998","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.06998","pdf_url":"https://arxiv.org/pdf/2505.06998","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"study":[1],"of":[2,5,14,127,156],"interlayer":[3,72,128],"similarity":[4,42,73,87,129],"multiplex":[6,62],"networks":[7,110],"helps":[8],"to":[9],"understand":[10],"the":[11,57,116,125,152],"intrinsic":[12],"structure":[13],"complex":[15,157],"systems,":[16],"revealing":[17],"how":[18],"changes":[19],"in":[20,55,61,136,150],"one":[21],"layer":[22],"can":[23],"propagate":[24],"and":[25,35,88,108,143,154],"affect":[26],"others,":[27],"thus":[28],"enabling":[29],"broad":[30],"implications":[31],"for":[32,99],"transportation,":[33],"social,":[34],"biological":[36],"systems.":[37,102,158],"Existing":[38],"algorithms":[39],"that":[40,112],"measure":[41],"between":[43,120],"network":[44,141,144],"layers":[45],"typically":[46],"encode":[47],"only":[48],"partial":[49],"information,":[50],"which":[51],"limits":[52],"their":[53],"effectiveness":[54],"capturing":[56],"full":[58],"complexity":[59],"inherent":[60],"networks.":[63],"To":[64],"address":[65],"this":[66],"limitation,":[67],"we":[68],"propose":[69],"a":[70,95],"novel":[71],"measuring":[74],"approach":[75],"named":[76],"Embedding":[77],"Aided":[78],"inTerlayer":[79],"Similarity":[80],"(EATSim).":[81],"EATSim":[82,113,132],"concurrently":[83],"incorporates":[84],"intralayer":[85],"structural":[86],"cross-layer":[89],"anchor":[90],"node":[91],"alignment":[92],"consistency,":[93],"providing":[94],"more":[96],"comprehensive":[97],"framework":[98],"analyzing":[100],"interconnected":[101,121],"Extensive":[103],"experiments":[104],"on":[105],"both":[106],"synthetic":[107],"real-world":[109],"demonstrate":[111],"effectively":[114],"captures":[115],"underlying":[117],"geometric":[118],"similarities":[119],"networks,":[122],"significantly":[123],"improving":[124],"accuracy":[126],"measurement.":[130],"Moreover,":[131],"achieves":[133],"state-of-the-art":[134],"performance":[135],"two":[137],"downstream":[138],"applications:":[139],"predicting":[140],"robustness":[142],"reducibility,":[145],"showing":[146],"its":[147],"great":[148],"potential":[149],"enhancing":[151],"understanding":[153],"management":[155]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}