{"id":"https://openalex.org/W6922510128","doi":"https://doi.org/10.13016/9ual-ikfo","title":"Structured discovery in graphs: Recommender systems and temporal graph analysis","display_name":"Structured discovery in graphs: Recommender systems and temporal graph analysis","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W6922510128","doi":"https://doi.org/10.13016/9ual-ikfo"},"language":"en","primary_location":{"id":"pmh:oai:drum.lib.umd.edu:1903/32855","is_oa":true,"landing_page_url":"http://hdl.handle.net/1903/32855","pdf_url":"https://drum.lib.umd.edu/bitstreams/3c00d0ac-6bf9-491b-afab-9f551a4a3ecb/download","source":{"id":"https://openalex.org/S4306401518","display_name":"University Libraries (University of Maryland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"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":"Dissertation"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://drum.lib.umd.edu/bitstreams/3c00d0ac-6bf9-491b-afab-9f551a4a3ecb/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Peyman, Sheyda Do'a","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Peyman, Sheyda Do'a","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":true,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.426800012588501,"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":0.426800012588501,"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":0.40560001134872437,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.048900000751018524,"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/homophily","display_name":"Homophily","score":0.6326000094413757},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6309999823570251},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5649999976158142},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5267000198364258},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.4000999927520752},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.39010000228881836},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.3837999999523163},{"id":"https://openalex.org/keywords/network-analysis","display_name":"Network analysis","score":0.37560001015663147},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.35929998755455017},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.34689998626708984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7163000106811523},{"id":"https://openalex.org/C2779812341","wikidata":"https://www.wikidata.org/wiki/Q5891525","display_name":"Homophily","level":2,"score":0.6326000094413757},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6309999823570251},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.545799970626831},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5267000198364258},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.4000999927520752},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3846000134944916},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.36809998750686646},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36000001430511475},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.35929998755455017},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.33809998631477356},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33379998803138733},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3183000087738037},{"id":"https://openalex.org/C114713312","wikidata":"https://www.wikidata.org/wiki/Q7551269","display_name":"Social network analysis","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.2782000005245209},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C2777759810","wikidata":"https://www.wikidata.org/wiki/Q149316","display_name":"Lemma (botany)","level":3,"score":0.2572999894618988},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C2776050585","wikidata":"https://www.wikidata.org/wiki/Q7439360","display_name":"Scrutiny","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C137753397","wikidata":"https://www.wikidata.org/wiki/Q2434424","display_name":"Network science","level":3,"score":0.250900000333786}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:drum.lib.umd.edu:1903/32855","is_oa":true,"landing_page_url":"http://hdl.handle.net/1903/32855","pdf_url":"https://drum.lib.umd.edu/bitstreams/3c00d0ac-6bf9-491b-afab-9f551a4a3ecb/download","source":{"id":"https://openalex.org/S4306401518","display_name":"University Libraries (University of Maryland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"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":"Dissertation"},{"id":"doi:10.13016/9ual-ikfo","is_oa":true,"landing_page_url":"https://doi.org/10.13016/9ual-ikfo","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"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":"Collection"}],"best_oa_location":{"id":"pmh:oai:drum.lib.umd.edu:1903/32855","is_oa":true,"landing_page_url":"http://hdl.handle.net/1903/32855","pdf_url":"https://drum.lib.umd.edu/bitstreams/3c00d0ac-6bf9-491b-afab-9f551a4a3ecb/download","source":{"id":"https://openalex.org/S4306401518","display_name":"University Libraries (University of Maryland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"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":"Dissertation"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality","score":0.5004799962043762}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W6922510128.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph-valued":[0],"data":[1],"arises":[2],"in":[3,51,117,148,157,178,195,221,243],"numerous":[4],"diverse":[5],"scientific":[6],"fields":[7],"ranging":[8],"from":[9,130],"sociology,":[10],"epidemiology":[11],"and":[12,16,36,45,49,63,83,171,177,214],"genomics":[13],"to":[14,23,68,105,140,167,225,239],"neuroscience":[15],"economics.For":[17],"example,":[18],"sociologists":[19],"have":[20,46,55,65],"used":[21,66],"graphs":[22,67],"examine":[24],"the":[25,39,81,131,145,149,163,172,179,183,186,196,211,216,222,231],"roles":[26],"of":[27,41,74,85,98,136,143,151,198,218],"user":[28],"attributes":[29],"(gender,":[30],"class,":[31],"year)":[32],"at":[33],"American":[34],"colleges":[35],"universities":[37],"through":[38],"study":[40],"Facebook":[42],"friendship":[43],"networks":[44],"studied":[47],"segregation":[48],"homophily":[50],"social":[52],"networks;":[53],"epidemiologists":[54],"recently":[56],"modeled":[57],"Human-nCov":[58],"protein-protein":[59],"interactions":[60],"via":[61],"graphs,":[62,75],"neuroscientists":[64],"model":[69],"neuronal":[70],"connectomes.":[71],"The":[72,190],"structure":[73,147],"including":[76,160],"latent":[77,123,146],"features,":[78,116],"relationships":[79],"between":[80],"vertex":[82,87,199],"importance":[84],"each":[86],"are":[88,95],"all":[89],"highly":[90],"important":[91],"graph":[92,99,164,236],"properties":[93],"that":[94,126],"main":[96,134],"aspects":[97],"analysis/inference.":[100],"While":[101],"it":[102],"is":[103,139,165,174,185,193],"common":[104],"imbue":[106],"nodes":[107],"and/or":[108],"edges":[109],"with":[110],"implicitly":[111],"observed":[112],"numeric":[113],"or":[114],"qualitative":[115],"this":[118,137],"work":[119],"we":[120,204,234],"will":[121],"consider":[122,205],"network":[124,132,152,244],"features":[125],"must":[127],"be":[128],"estimated":[129],"topology.The":[133],"focus":[135],"text":[138],"find":[141],"ways":[142],"extracting":[144],"presence":[150],"anomalies.":[153],"These":[154],"anomalies":[155,242],"occur":[156],"different":[158],"scenarios:":[159],"cases":[161],"when":[162,181],"subject":[166],"an":[168],"adversarial":[169,212,228],"attack":[170],"anomaly":[173,184],"inhibiting":[175],"inference,":[176],"scenario":[180],"detecting":[182],"key":[187],"inference":[188],"task.":[189],"former":[191],"case":[192,233],"explored":[194],"context":[197],"nomination":[200],"information":[201],"retrieval,":[202],"where":[203],"both":[206],"analytic":[207],"methods":[208,238],"for":[209],"countering":[210],"noise":[213],"also":[215],"addition":[217],"a":[219],"user-in-the-loop":[220],"retrieval":[223],"algorithm":[224],"counter":[226],"potential":[227],"noise.":[229],"In":[230],"latter":[232],"use":[235],"embedding":[237],"discover":[240],"sequential":[241],"time":[245],"series.":[246]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
