{"id":"https://openalex.org/W2987360131","doi":"https://doi.org/10.1145/3357384.3357979","title":"Learning to Identify High Betweenness Centrality Nodes from Scratch","display_name":"Learning to Identify High Betweenness Centrality Nodes from Scratch","publication_year":2019,"publication_date":"2019-11-03","ids":{"openalex":"https://openalex.org/W2987360131","doi":"https://doi.org/10.1145/3357384.3357979","mag":"2987360131"},"language":"en","primary_location":{"id":"doi:10.1145/3357384.3357979","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102982966","display_name":"Changjun Fan","orcid":"https://orcid.org/0000-0003-4846-8983"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]},{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Changjun Fan","raw_affiliation_strings":["National University of Defense Technology & University of California, Los Angeles, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology & University of California, Los Angeles, Changsha, China","institution_ids":["https://openalex.org/I170215575","https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101570687","display_name":"Li Zeng","orcid":"https://orcid.org/0000-0002-4219-788X"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zeng","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060223403","display_name":"Yuhui Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhui Ding","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102861481","display_name":"Muhao Chen","orcid":"https://orcid.org/0000-0003-0118-3147"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]},{"id":"https://openalex.org/I36788626","display_name":"California University of Pennsylvania","ror":"https://ror.org/01spssf70","country_code":"US","type":"education","lineage":["https://openalex.org/I36788626"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Muhao Chen","raw_affiliation_strings":["University of California, Los Angeles & University of Pennsylvania, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles & University of Pennsylvania, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I36788626","https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025213473","display_name":"Yizhou Sun","orcid":"https://orcid.org/0000-0003-1812-6843"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yizhou Sun","raw_affiliation_strings":["University of California, Los Angeles, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115588589","display_name":"Zhong Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhong Liu","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.1813,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.94782392,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"559","last_page":"568"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9961000084877014,"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/T11476","display_name":"Graph theory and applications","score":0.9506000280380249,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/betweenness-centrality","display_name":"Betweenness centrality","score":0.8400880098342896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7517391443252563},{"id":"https://openalex.org/keywords/centrality","display_name":"Centrality","score":0.562237024307251},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4792092740535736},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4547249376773834},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4503544270992279},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.4366511404514313},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4220837652683258},{"id":"https://openalex.org/keywords/network-science","display_name":"Network science","score":0.41233810782432556},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37167590856552124},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3432523012161255},{"id":"https://openalex.org/keywords/complex-network","display_name":"Complex network","score":0.34030449390411377},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.21753177046775818},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13852331042289734}],"concepts":[{"id":"https://openalex.org/C117045392","wikidata":"https://www.wikidata.org/wiki/Q4899215","display_name":"Betweenness centrality","level":3,"score":0.8400880098342896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7517391443252563},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.562237024307251},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4792092740535736},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4547249376773834},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4503544270992279},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.4366511404514313},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4220837652683258},{"id":"https://openalex.org/C137753397","wikidata":"https://www.wikidata.org/wiki/Q2434424","display_name":"Network science","level":3,"score":0.41233810782432556},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37167590856552124},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3432523012161255},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.34030449390411377},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.21753177046775818},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13852331042289734},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3357384.3357979","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1029409534","https://openalex.org/W1888005072","https://openalex.org/W1981000476","https://openalex.org/W2026363362","https://openalex.org/W2046121707","https://openalex.org/W2071178550","https://openalex.org/W2112556110","https://openalex.org/W2151936673","https://openalex.org/W2154851992","https://openalex.org/W2171707538","https://openalex.org/W2344526012","https://openalex.org/W2388242991","https://openalex.org/W2519887557","https://openalex.org/W2572289316","https://openalex.org/W2577283662","https://openalex.org/W2621917836","https://openalex.org/W2792234394","https://openalex.org/W2884593183","https://openalex.org/W2950898568","https://openalex.org/W2962756421","https://openalex.org/W2963794747","https://openalex.org/W2963858333","https://openalex.org/W3102136874","https://openalex.org/W3102794461","https://openalex.org/W3104097132","https://openalex.org/W3105705953"],"related_works":["https://openalex.org/W1973509935","https://openalex.org/W4389076551","https://openalex.org/W2611574733","https://openalex.org/W2514739320","https://openalex.org/W2107855069","https://openalex.org/W2806008330","https://openalex.org/W2121500878","https://openalex.org/W4385357749","https://openalex.org/W2046736422","https://openalex.org/W3174053386"],"abstract_inverted_index":{"Betweenness":[0],"centrality":[1,7],"(BC)":[2],"is":[3,36,54,120,222],"a":[4,21,117,138,148,181,189],"widely":[5],"used":[6],"measures":[8],"for":[9,231],"network":[10,22,46,126,155],"analysis,":[11],"which":[12,119,165,183],"seeks":[13],"to":[14,38,58,70,94,98,124,157,202,206,229,251],"describe":[15],"the":[16,26,73,99,107,151,154,167,172,185,198,204,208,220,238,259,269],"importance":[17],"of":[18,25,28,75,110,171,188,193,210,224,273],"nodes":[19,112,211,230],"in":[20,23,116,191,264,271],"terms":[24,192,272],"fraction":[27],"shortest":[29],"paths":[30],"that":[31],"pass":[32],"through":[33],"them.":[34],"It":[35],"key":[37],"many":[39,125],"valuable":[40],"applications,":[41],"including":[42],"community":[43],"detection":[44],"and":[45,89,141,235,245,266],"dismantling.":[47],"Computing":[48],"BC":[49,115,227],"scores":[50,228],"on":[51,86,106,217,242,275],"large":[52,277],"networks":[53,247],"computationally":[55],"challenging":[56],"due":[57],"its":[59,194],"high":[60],"time":[61,85],"complexity.":[62],"Many":[63],"sampling-based":[64],"approximation":[65],"algorithms":[66],"have":[67],"been":[68],"proposed":[69],"speed":[71],"up":[72,258],"estimation":[74],"BC.":[76,195,214],"However,":[77],"these":[78],"methods":[79],"still":[80],"need":[81],"considerable":[82],"long":[83],"running":[84],"large-scale":[87],"networks,":[88,219,234],"their":[90,213],"results":[91],"are":[92],"sensitive":[93],"even":[95,267],"small":[96],"perturbation":[97],"networks.":[100,279],"In":[101],"this":[102,135],"paper,":[103],"we":[104,133],"focus":[105],"efficient":[108],"identification":[109],"top-k":[111],"with":[113],"highest":[114],"graph,":[118],"an":[121,143,162],"essential":[122],"task":[123,136],"applications.":[127],"Different":[128],"from":[129],"previous":[130],"heuristic":[131],"methods,":[132],"turn":[134],"into":[137,180],"learning":[139],"problem":[140],"design":[142],"encoder-decoder":[144],"based":[145],"framework":[146],"as":[147,161],"solution.":[149],"Specifically,":[150],"encoder":[152],"leverages":[153],"structure":[156],"represent":[158],"each":[159,177],"node":[160,190],"embedding":[163,178],"vector,":[164],"captures":[166],"important":[168],"structural":[169],"information":[170],"node.":[173],"The":[174],"decoder":[175],"transforms":[176],"vector":[179],"scalar,":[182],"identifies":[184],"relative":[186,226],"rank":[187],"We":[196],"use":[197],"pairwise":[199],"ranking":[200],"loss":[201],"train":[203],"model":[205,221,255],"identify":[207],"orders":[209],"regarding":[212],"By":[215],"training":[216],"small-scale":[218],"capable":[223],"assigning":[225],"much":[232],"larger":[233],"thus":[236],"identifying":[237],"highly-ranked":[239],"nodes.":[240],"Experiments":[241],"both":[243],"synthetic":[244],"real-world":[246,278],"demonstrate":[248],"that,":[249],"compared":[250],"existing":[252],"baselines,":[253],"our":[254],"drastically":[256],"speeds":[257],"prediction":[260],"without":[261],"noticeable":[262],"sacrifice":[263],"accuracy,":[265],"outperforms":[268],"state-of-the-arts":[270],"accuracy":[274],"several":[276]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}