{"id":"https://openalex.org/W6887777722","doi":"https://doi.org/10.17615/4m8z-3122","title":"METHOD OF MOMENTS FOR EXPONENTIAL RANDOM GRAPH MODEL SELECTION","display_name":"METHOD OF MOMENTS FOR EXPONENTIAL RANDOM GRAPH MODEL SELECTION","publication_year":2024,"publication_date":"2024-05-21","ids":{"openalex":"https://openalex.org/W6887777722","doi":"https://doi.org/10.17615/4m8z-3122"},"language":"en","primary_location":{"id":"doi:10.17615/4m8z-3122","is_oa":true,"landing_page_url":"https://doi.org/10.17615/4m8z-3122","pdf_url":null,"source":{"id":"https://openalex.org/S7407051488","display_name":"UNC Libraries","issn_l":null,"issn":null,"is_oa":false,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"thesis"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.17615/4m8z-3122","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"El-Zaatari, Helal","orcid":null},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"El-Zaatari, Helal","raw_affiliation_strings":["University of North Carolina at Chapel Hill"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114027177"],"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":true,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.45100000500679016,"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":0.45100000500679016,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.14949999749660492,"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/T13283","display_name":"Mental Health Research Topics","score":0.058400001376867294,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/exponential-random-graph-models","display_name":"Exponential random graph models","score":0.8855999708175659},{"id":"https://openalex.org/keywords/centrality","display_name":"Centrality","score":0.5900999903678894},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5038999915122986},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.4862000048160553},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.45899999141693115},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.40540000796318054},{"id":"https://openalex.org/keywords/social-network-analysis","display_name":"Social network analysis","score":0.39969998598098755},{"id":"https://openalex.org/keywords/network-science","display_name":"Network science","score":0.36570000648498535},{"id":"https://openalex.org/keywords/social-network","display_name":"Social network (sociolinguistics)","score":0.36250001192092896}],"concepts":[{"id":"https://openalex.org/C30549945","wikidata":"https://www.wikidata.org/wiki/Q5421526","display_name":"Exponential random graph models","level":4,"score":0.8855999708175659},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.5900999903678894},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5458999872207642},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5038999915122986},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.4862000048160553},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.45899999141693115},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C114713312","wikidata":"https://www.wikidata.org/wiki/Q7551269","display_name":"Social network analysis","level":3,"score":0.39969998598098755},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3871000111103058},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37389999628067017},{"id":"https://openalex.org/C137753397","wikidata":"https://www.wikidata.org/wiki/Q2434424","display_name":"Network science","level":3,"score":0.36570000648498535},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.36250001192092896},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.35359999537467957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3531999886035919},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32010000944137573},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.31839999556541443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30559998750686646},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2955999970436096},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.274399995803833},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C28225019","wikidata":"https://www.wikidata.org/wiki/Q4915005","display_name":"Biological network","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.17615/4m8z-3122","is_oa":true,"landing_page_url":"https://doi.org/10.17615/4m8z-3122","pdf_url":null,"source":{"id":"https://openalex.org/S7407051488","display_name":"UNC Libraries","issn_l":null,"issn":null,"is_oa":false,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"thesis"}],"best_oa_location":{"id":"doi:10.17615/4m8z-3122","is_oa":true,"landing_page_url":"https://doi.org/10.17615/4m8z-3122","pdf_url":null,"source":{"id":"https://openalex.org/S7407051488","display_name":"UNC Libraries","issn_l":null,"issn":null,"is_oa":false,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"thesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Collaboration":[0],"has":[1],"become":[2],"crucial":[3],"in":[4,8,18,65,80,174,185,219,287,319,348],"solving":[5],"scientific":[6,234],"problems":[7],"biomedical":[9],"and":[10,30,40,76,109,121,135,153,181,189,199,243,271,291,310,337,341],"health":[11,81,175],"sciences.":[12],"There":[13],"is":[14,204,301],"a":[15,36,71,88,93,186,212,249,332,343],"growing":[16],"interest":[17],"applying":[19],"social":[20,45,200,230,339],"network":[21,66,105,159,273,289,297,322,329,350],"analysis":[22,228,351],"to":[23,27,57,74,156,179,195,206,225,278,307,328],"professional":[24,207],"associations":[25],"aiming":[26],"leverage":[28],"expertise":[29],"resources":[31],"for":[32,43,62,104,215,335,345],"optimal":[33],"synergy.":[34],"As":[35],"set":[37],"of":[38,60,87,95,102,171,229,264,269,304,316],"computational":[39,244],"statistical":[41,349],"methods":[42],"analyzing":[44],"networks,":[46,312],"Exponential":[47,220],"Random":[48,221],"Graph":[49,222],"Models":[50,223],"(ERGMs)":[51,224],"examine":[52,196],"complex":[53,296],"collaborative":[54,187],"networks":[55,231,282,340],"due":[56],"their":[58],"uniqueness":[59],"allowing":[61],"non-independent":[63],"variables":[64,137],"modeling.":[67],"This":[68,209,255],"study":[69],"took":[70],"review":[72],"approach":[73,256],"collect":[75],"analyze":[77],"ERGM":[78,108,172,241],"applications":[79,173],"sciences":[82],"by":[83],"following":[84],"the":[85,128,149,157,167,227,259,267,284,302,314],"protocol":[86],"systematic":[89,250],"review.":[90],"We":[91,98],"included":[92],"total":[94],"30":[96],"studies.":[97],"observed":[99],"five":[100],"types":[101],"ERGMs":[103,318],"modeling":[106,336],"(standard":[107],"its":[110],"extensions":[111],"such":[112,239],"as":[113,240],"Bayesian":[114],"ERGM,":[115,117,120,130],"Temporal":[116,119],"Separable":[118],"Multilevel":[122],"ERGM).":[123],"Most":[124],"studies":[125,194],"(80%)":[126],"used":[127],"standard":[129],"which":[131,203],"possesses":[132],"only":[133],"endogenous":[134,216],"exogenous":[136],"examining":[138],"either":[139],"micro-":[140],"(individual-based)":[141],"or":[142],"macro-level":[143],"(organization-based)":[144],"collaborations":[145],"without":[146],"exploring":[147],"how":[148],"links":[150],"between":[151],"individuals":[152],"organizations":[154],"contribute":[155,326],"overall":[158],"structure.":[160],"Our":[161],"findings":[162,325],"help":[163],"researchers":[164],"(a)":[165],"understand":[166],"extant":[168],"research":[169],"landscape":[170],"sciences,":[176],"(b)":[177],"learn":[178],"control":[180],"predict":[182],"connection":[183],"occurrence":[184],"network,":[188],"(c)":[190],"better":[191],"design":[192],"ERGM-applied":[193],"com-plex":[197],"relations":[198],"system":[201],"structure,":[202],"native":[205],"collaborations.":[208],"dissertation":[210],"introduces":[211],"novel":[213],"methodology":[214,306],"variable":[217],"selection":[218,253],"enhance":[226],"across":[232],"various":[233],"disciplines.":[235],"Addressing":[236],"critical":[237],"challenges":[238],"degeneracy":[242],"complexity,":[245],"our":[246],"method":[247],"integrates":[248],"stepwise":[251],"feature":[252],"process.":[254],"effectively":[257],"manages":[258],"intractable":[260],"normalizing":[261],"constants":[262],"characteristic":[263],"ERGMs,":[265],"ensuring":[266],"generation":[268],"accurate":[270],"non-degenerate":[272],"models.":[274],"An":[275],"empirical":[276],"application":[277],"ten":[279],"real-life":[280],"binary":[281],"demonstrates":[283],"method's":[285],"effectiveness":[286],"accommodating":[288],"dependencies":[290],"providing":[292],"meaningful":[293],"insights":[294],"into":[295],"interactions.":[298],"Particularly":[299],"notable":[300],"adaptability":[303],"this":[305],"both":[308],"directed":[309],"undirected":[311],"overcoming":[313],"limitations":[315],"traditional":[317],"capturing":[320],"realistic":[321],"structures.":[323],"The":[324],"significantly":[327],"analysis,":[330],"offering":[331],"robust":[333],"framework":[334],"interpreting":[338],"laying":[342],"foundation":[344],"future":[346],"advancements":[347],"techniques.":[352]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
