{"id":"https://openalex.org/W3116301335","doi":"https://doi.org/10.1145/3437963.3441767","title":"Towards Scalable Spectral Embedding and Data Visualization via Spectral Coarsening","display_name":"Towards Scalable Spectral Embedding and Data Visualization via Spectral Coarsening","publication_year":2021,"publication_date":"2021-03-06","ids":{"openalex":"https://openalex.org/W3116301335","doi":"https://doi.org/10.1145/3437963.3441767","mag":"3116301335"},"language":"en","primary_location":{"id":"doi:10.1145/3437963.3441767","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437963.3441767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","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/A5060994033","display_name":"Zhiqiang Zhao","orcid":"https://orcid.org/0000-0001-7239-6604"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhiqiang Zhao","raw_affiliation_strings":["Stevens Institute of Technology, Hoboken, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100386104","display_name":"Ying Zhang","orcid":"https://orcid.org/0000-0002-2674-1638"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Zhang","raw_affiliation_strings":["Stevens Institute of Technology, Hoboken, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008259799","display_name":"Zhuo Feng","orcid":"https://orcid.org/0000-0002-2989-2597"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuo Feng","raw_affiliation_strings":["Stevens Institute of Technology, Hoboken, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060994033"],"corresponding_institution_ids":["https://openalex.org/I108468826"],"apc_list":null,"apc_paid":null,"fwci":1.4439,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.81281166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"869","last_page":"877"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9983999729156494,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.988099992275238,"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/embedding","display_name":"Embedding","score":0.8078743815422058},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.7699156999588013},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7229239344596863},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7148207426071167},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.7109932899475098},{"id":"https://openalex.org/keywords/graph-drawing","display_name":"Graph drawing","score":0.6163663864135742},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5907167196273804},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5579299926757812},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.5399063229560852},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5396506786346436},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5209358930587769},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4815126359462738},{"id":"https://openalex.org/keywords/power-graph-analysis","display_name":"Power graph analysis","score":0.4345044195652008},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2648458778858185},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.2537423372268677},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14593976736068726}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.8078743815422058},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7699156999588013},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7229239344596863},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7148207426071167},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.7109932899475098},{"id":"https://openalex.org/C112953755","wikidata":"https://www.wikidata.org/wiki/Q739462","display_name":"Graph drawing","level":3,"score":0.6163663864135742},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5907167196273804},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5579299926757812},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.5399063229560852},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5396506786346436},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5209358930587769},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4815126359462738},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.4345044195652008},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2648458778858185},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.2537423372268677},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14593976736068726},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3437963.3441767","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437963.3441767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2575898893","display_name":null,"funder_award_id":"1350206, 1618364, 1909105","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W324754135","https://openalex.org/W1501565421","https://openalex.org/W1506342804","https://openalex.org/W1543491698","https://openalex.org/W1571321879","https://openalex.org/W1875842236","https://openalex.org/W1916091028","https://openalex.org/W1983193888","https://openalex.org/W1999170091","https://openalex.org/W1999446611","https://openalex.org/W2019040312","https://openalex.org/W2051540665","https://openalex.org/W2055935910","https://openalex.org/W2059985735","https://openalex.org/W2060181055","https://openalex.org/W2061010610","https://openalex.org/W2067081844","https://openalex.org/W2070232376","https://openalex.org/W2070448527","https://openalex.org/W2096250025","https://openalex.org/W2101491865","https://openalex.org/W2117909080","https://openalex.org/W2121947440","https://openalex.org/W2129575457","https://openalex.org/W2130790725","https://openalex.org/W2132914434","https://openalex.org/W2135957668","https://openalex.org/W2153441719","https://openalex.org/W2164853353","https://openalex.org/W2169876669","https://openalex.org/W2396255976","https://openalex.org/W2468907370","https://openalex.org/W2559655401","https://openalex.org/W2594480752","https://openalex.org/W2624677037","https://openalex.org/W2769943877","https://openalex.org/W2772181175","https://openalex.org/W2888497928","https://openalex.org/W2945024758","https://openalex.org/W2951527381","https://openalex.org/W2960658350","https://openalex.org/W2962910558","https://openalex.org/W2963318411","https://openalex.org/W2971144780","https://openalex.org/W3122153094","https://openalex.org/W4212774754","https://openalex.org/W4249528565","https://openalex.org/W6633117260","https://openalex.org/W6634131805","https://openalex.org/W6659374036","https://openalex.org/W6714497132","https://openalex.org/W6758192066"],"related_works":["https://openalex.org/W1804383502","https://openalex.org/W2182728651","https://openalex.org/W91435914","https://openalex.org/W1998703808","https://openalex.org/W2294734161","https://openalex.org/W1970747620","https://openalex.org/W4283123191","https://openalex.org/W2525134017","https://openalex.org/W2031730653","https://openalex.org/W2903996962"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,38,78,109],"scalable":[4],"multilevel":[5,60,140],"framework":[6],"for":[7,52,59,139,150],"the":[8,27,33,46,53,97,128],"spectral":[9,29,41,61,141],"embedding":[10,63,143],"of":[11,32,55,69,80,153],"large":[12,70,81,154],"undirected":[13],"graphs.":[14],"The":[15],"proposed":[16],"method":[17],"first":[18],"computes":[19],"much":[20,56,110],"smaller":[21,111],"yet":[22],"sparse":[23],"graphs":[24,49,82,130],"while":[25],"preserving":[26],"key":[28],"(structural)":[30],"properties":[31],"original":[34],"graph,":[35],"by":[36],"exploiting":[37],"nearly-linear":[39],"time":[40],"graph":[42,62,99,112,142],"coarsening":[43],"approach.":[44],"Then,":[45],"resultant":[47],"spectrally-coarsened":[48,129],"are":[50,93],"leveraged":[51],"development":[54],"faster":[57],"algorithms":[58],"(clustering)":[64,144],"as":[65,67],"well":[66],"visualization":[68,152],"data":[71,155],"sets.":[72,156],"We":[73],"conducted":[74],"extensive":[75],"experiments":[76],"using":[77],"variety":[79],"and":[83,85,104,119,145],"datasets":[84],"obtained":[86],"very":[87],"promising":[88],"results.":[89],"For":[90],"instance,":[91],"we":[92],"able":[94],"to":[95,133,136,147],"coarsen":[96],"\"coPapersCiteseer\"":[98],"with":[100,113],"0.43":[101],"million":[102,106],"nodes":[103,118],"16":[105,126],"edges":[107,123],"into":[108],"only":[114],"13K":[115],"(32X":[116],"fewer)":[117,122],"17K":[120],"(950X":[121],"in":[124],"about":[125],"seconds;":[127],"allow":[131],"us":[132],"achieve":[134],"up":[135,146],"1,100X":[137],"speedup":[138,149],"60X":[148],"t-SNE":[151]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-24T08:23:43.765630","created_date":"2025-10-10T00:00:00"}