{"id":"https://openalex.org/W3169244474","doi":"https://doi.org/10.1109/bigdata52589.2021.9671784","title":"A Pre-training Oracle for Predicting Distances in Social Networks","display_name":"A Pre-training Oracle for Predicting Distances in Social Networks","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W3169244474","doi":"https://doi.org/10.1109/bigdata52589.2021.9671784","mag":"3169244474"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671784","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2106.03233","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032922962","display_name":"Gunjan Mahindre","orcid":"https://orcid.org/0000-0003-1092-6512"},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gunjan Mahindre","raw_affiliation_strings":["Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA","institution_ids":["https://openalex.org/I92446798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055293673","display_name":"Rasika Karkare","orcid":"https://orcid.org/0000-0001-8149-5292"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rasika Karkare","raw_affiliation_strings":["Data Science, Worcester Polytechnic Institute, Worcester, MA, USA"],"affiliations":[{"raw_affiliation_string":"Data Science, Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027655988","display_name":"Randy Paffenroth","orcid":"https://orcid.org/0000-0002-4823-1348"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Randy Paffenroth","raw_affiliation_strings":["Mathematical Sciences, Computer Science & Data Science, Worcester Polytechnic Institute, Worcester, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mathematical Sciences, Computer Science & Data Science, Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026739162","display_name":"Anura P. Jayasumana","orcid":"https://orcid.org/0000-0002-8335-655X"},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anura Jayasumana","raw_affiliation_strings":["Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA","institution_ids":["https://openalex.org/I92446798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5032922962"],"corresponding_institution_ids":["https://openalex.org/I92446798"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06261181,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"213","issue":null,"first_page":"4126","last_page":"4135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9991000294685364,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.7812763452529907},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7566047310829163},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5638109445571899},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.557133138179779},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.544477105140686},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5146034955978394},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4873140752315521},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4560825824737549},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4415220320224762},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4389254152774811},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.4166800379753113},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3246683180332184}],"concepts":[{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.7812763452529907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7566047310829163},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5638109445571899},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.557133138179779},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.544477105140686},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5146034955978394},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4873140752315521},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4560825824737549},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4415220320224762},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4389254152774811},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.4166800379753113},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3246683180332184},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671784","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2106.03233","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.03233","pdf_url":"https://arxiv.org/pdf/2106.03233","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3169244474","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2106.03233","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2106.03233","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2106.03233","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2106.03233","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.03233","pdf_url":"https://arxiv.org/pdf/2106.03233","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7599999904632568,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3169244474.pdf","grobid_xml":"https://content.openalex.org/works/W3169244474.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1746637951","https://openalex.org/W2026014023","https://openalex.org/W2039750798","https://openalex.org/W2044888216","https://openalex.org/W2095724373","https://openalex.org/W2099021783","https://openalex.org/W2124637492","https://openalex.org/W2131492273","https://openalex.org/W2132013054","https://openalex.org/W2132022337","https://openalex.org/W2211659822","https://openalex.org/W2295520632","https://openalex.org/W2606321545","https://openalex.org/W2611328865","https://openalex.org/W2624407581","https://openalex.org/W2767544505","https://openalex.org/W2794363675","https://openalex.org/W2801982793","https://openalex.org/W2917193993","https://openalex.org/W2937901855","https://openalex.org/W2993869496","https://openalex.org/W3006451817","https://openalex.org/W3013788610","https://openalex.org/W3122280190","https://openalex.org/W3123976468","https://openalex.org/W6680300913","https://openalex.org/W6747954111","https://openalex.org/W6775775685","https://openalex.org/W6789086080"],"related_works":["https://openalex.org/W2949598554","https://openalex.org/W2282523189","https://openalex.org/W2963167917","https://openalex.org/W3160511242","https://openalex.org/W3200511797","https://openalex.org/W3174315735","https://openalex.org/W2189214667","https://openalex.org/W2776414022","https://openalex.org/W2918420510","https://openalex.org/W3132772989","https://openalex.org/W2096995231","https://openalex.org/W3169544834","https://openalex.org/W3089801830","https://openalex.org/W2587314205","https://openalex.org/W2105507161","https://openalex.org/W2787017828","https://openalex.org/W3179439402","https://openalex.org/W3097264851","https://openalex.org/W3118589287","https://openalex.org/W2803823445"],"abstract_inverted_index":{"In":[0,196],"this":[1],"paper,":[2],"we":[3,24,88,162,198],"propose":[4],"a":[5,21,26,40,46,124,134,140,200],"novel":[6],"method":[7],"to":[8,56,81,101,108,146,223,235,243],"make":[9],"distance":[10,188],"predictions":[11,114],"in":[12,128],"real-world":[13,47,116,121,166],"social":[14,117,216],"networks.":[15,61],"As":[16],"predicting":[17],"missing":[18],"distances":[19,213],"is":[20],"difficult":[22],"problem,":[23],"take":[25],"two-stage":[27],"approach.":[28],"Structural":[29],"parameters":[30,73,157],"for":[31,67,142],"families":[32],"of":[33,43,45,105,203,211],"synthetic":[34,51,71,148,237],"networks":[35,52,122,172,229],"are":[36,53],"first":[37,65],"estimated":[38],"from":[39,214],"small":[41],"set":[42],"measurements":[44],"network":[48,247],"and":[49,169,173,186,240],"these":[50],"then":[54],"used":[55,77],"pre-train":[57],"the":[58,68,103,106,143,152,215],"predictive":[59],"neural":[60],"Since":[62],"our":[63,90],"model":[64,137,234],"searches":[66],"most":[69],"suitable":[70],"graph":[72,156],"which":[74],"can":[75,138,158,219],"be":[76,159,220],"as":[78,192,227],"an":[79,97,232],"\u201coracle\u201d":[80],"create":[82],"arbitrarily":[83],"large":[84],"training":[85,110,238],"data":[86,111],"sets,":[87],"call":[89],"approach":[91,98],"\u201cOracle":[92],"Search":[93],"Pre-training\u201d":[94],"(OSP).":[95],"Such":[96],"enables":[99],"us":[100],"evaluate":[102],"robustness":[104],"autoencoder":[107],"artificial":[109],"while":[112],"making":[113],"on":[115,165],"networks.For":[118],"example,":[119],"many":[120,245],"exhibit":[123],"Power":[125,135,154],"law":[126,136,155],"structure":[127],"their":[129],"node":[130],"degree":[131],"distribution,":[132],"so":[133],"provide":[139],"foundation":[141],"desired":[144],"oracle":[145],"generate":[147,236],"pre-training":[149],"networks,":[150],"if":[151],"appropriate":[153,233],"estimated.":[160],"Accordingly,":[161],"conduct":[163],"experiments":[164],"Facebook,":[167],"Email,":[168],"Train":[170],"Bombing":[171],"show":[174],"that":[175],"OSP":[176,218],"outperforms":[177],"models":[178,181],"without":[179],"pre-training,":[180],"pre-trained":[182],"with":[183,208],"inaccurate":[184],"parameters,":[185],"other":[187,224],"prediction":[189,201],"schemes":[190],"such":[191,226],"Low-rank":[193],"Matrix":[194],"Completion.":[195],"particular,":[197],"achieve":[199],"error":[202],"less":[204],"than":[205],"one":[206],"hop":[207],"only":[209],"1%":[210],"sampled":[212],"network.":[217],"easily":[221],"extended":[222],"domains":[225],"random":[228],"by":[230],"choosing":[231],"data,":[239],"therefore":[241],"promises":[242],"impact":[244],"different":[246],"learning":[248],"problems.":[249]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2025-10-10T00:00:00"}
