{"id":"https://openalex.org/W2170682041","doi":null,"title":"Learning a Distance Metric from a Network","display_name":"Learning a Distance Metric from a Network","publication_year":2011,"publication_date":"2011-01-01","ids":{"openalex":"https://openalex.org/W2170682041","mag":"2170682041"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.221.6753","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.6753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.columbia.edu/%7Ejebara/papers/metricfromnetwork.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Blake Shaw","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Blake Shaw","raw_affiliation_strings":["Computer Science Dept., Columbia University and Foursquare#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Dept., Columbia University and Foursquare#TAB#","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002044887","display_name":"Bert Huang","orcid":"https://orcid.org/0000-0002-8548-7246","observed_orcids":["https://orcid.org/0000-0002-8548-7246"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]},{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bert Huang","raw_affiliation_strings":["Computer Science Dept., Columbia University and University of Maryland#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Dept., Columbia University and University of Maryland#TAB#","institution_ids":["https://openalex.org/I66946132","https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068077704","display_name":"Tony Jebara","orcid":"https://orcid.org/0000-0003-0314-3376","observed_orcids":["https://orcid.org/0000-0003-0314-3376"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tony Jebara","raw_affiliation_strings":["Computer Science Department, Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Columbia University","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.7271,"has_fulltext":false,"cited_by_count":69,"citation_normalized_percentile":{"value":0.95626326,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9286999702453613,"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":0.9286999702453613,"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":0.05570000037550926,"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/T10057","display_name":"Face and Expression Recognition","score":0.008299999870359898,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metric-learning","display_name":"metric learning","score":0.7870000004768372},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.9509999752044678},{"id":"https://openalex.org/keywords/network-embedding","display_name":"graph embedding","score":0.8109999895095825},{"id":"https://openalex.org/keywords/link-prediction","display_name":"link prediction","score":0.8500000238418579},{"id":"https://openalex.org/keywords/node-attributes","display_name":"node attributes","score":0.7910000085830688},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.8059999942779541},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"stochastic gradient descent","score":0.7770000100135803},{"id":"https://openalex.org/keywords/scalability","display_name":"scalability","score":0.4090000092983246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.664638102054596},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6452915072441101},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5949201583862305},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.5935289859771729},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4899803698062897},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4633984863758087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42847907543182373},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38932159543037415},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.221.6753","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.6753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.columbia.edu/%7Ejebara/papers/metricfromnetwork.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.230.7367","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.230.7367","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://books.nips.cc/papers/files/nips24/NIPS2011_1070.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.297.9870","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.9870","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.columbia.edu/~jebara/papers/metricfromnetwork.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.909.9083","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.909.9083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://papers.nips.cc/paper/4392-learning-a-distance-metric-from-a-network.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W18156537","https://openalex.org/W1528845765","https://openalex.org/W1532499126","https://openalex.org/W1591139320","https://openalex.org/W1966139704","https://openalex.org/W1976618413","https://openalex.org/W1983345514","https://openalex.org/W2031248101","https://openalex.org/W2035720976","https://openalex.org/W2084809122","https://openalex.org/W2088658556","https://openalex.org/W2106053110","https://openalex.org/W2106545428","https://openalex.org/W2107107106","https://openalex.org/W2111708605","https://openalex.org/W2117154949","https://openalex.org/W2124672527","https://openalex.org/W2125993116","https://openalex.org/W2136664839","https://openalex.org/W2142623206","https://openalex.org/W2148606196","https://openalex.org/W2149580580","https://openalex.org/W2150256170","https://openalex.org/W2153280946","https://openalex.org/W2157579446"],"related_works":["https://openalex.org/W123328989","https://openalex.org/W2743754643","https://openalex.org/W2147578440","https://openalex.org/W2168485778","https://openalex.org/W7100738852","https://openalex.org/W1791066039","https://openalex.org/W7096306632","https://openalex.org/W3213766945","https://openalex.org/W7211866789","https://openalex.org/W7101136029"],"abstract_inverted_index":{"Many":[0],"real-world":[1,99],"networks":[2,146],"are":[3,44],"described":[4],"by":[5],"both":[6],"connectivity":[7,49,72,82],"information":[8],"and":[9,17,98,138,151],"features":[10,108],"for":[11,30,119],"every":[12],"node.":[13],"To":[14],"better":[15],"model":[16],"understand":[18],"these":[19],"networks,":[20],"we":[21],"present":[22],"structure":[23,50,59,68],"preserving":[24,60],"metric":[25,35,65],"learning":[26,31],"(SPML),":[27],"an":[28],"algorithm":[29,58,73],"a":[32,37,64,71,94,117],"Mahalanobis":[33],"distance":[34],"from":[36,88,106],"network":[38,137],"such":[39,74],"that":[40],"the":[41,47,52,55,80,86,89,129,133,136,140],"learned":[42,90],"distances":[43,87],"tied":[45],"to":[46,142,145],"inherent":[48],"of":[51,96,135,147,149,153],"network.":[53],"Like":[54],"graph":[56],"embed-ding":[57],"embedding,":[61],"SPML":[62,102,121],"learns":[63],"which":[66,127],"is":[67],"preserving,":[69],"meaning":[70],"as":[75],"k-nearest":[76],"neigh-bors":[77],"will":[78],"yield":[79],"correct":[81],"when":[83],"applied":[84],"using":[85],"metric.":[91],"We":[92,114],"show":[93],"variety":[95],"synthetic":[97],"experiments":[100],"where":[101],"predicts":[103],"link":[104],"patterns":[105],"node":[107],"more":[109],"accurately":[110],"than":[111],"stan-dard":[112],"techniques.":[113],"further":[115],"demonstrate":[116],"method":[118,141],"optimizing":[120],"based":[122],"on":[123,132],"stochastic":[124],"gradient":[125],"descent":[126],"removes":[128],"running-time":[130],"dependency":[131],"size":[134],"allows":[139],"easily":[143],"scale":[144],"thou-sands":[148],"nodes":[150],"millions":[152],"edges.":[154],"1":[155]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":8}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
