{"id":"https://openalex.org/W3012743266","doi":"https://doi.org/10.1145/3366423.3380059","title":"Using Cliques with Higher-order Spectral Embeddings Improves Graph Visualizations","display_name":"Using Cliques with Higher-order Spectral Embeddings Improves Graph Visualizations","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012743266","doi":"https://doi.org/10.1145/3366423.3380059","mag":"3012743266"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380059","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380059","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380059","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065393424","display_name":"Huda Nassar","orcid":"https://orcid.org/0000-0003-2909-5202"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Huda Nassar","raw_affiliation_strings":["Stanford University"],"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112515149","display_name":"Caitlin Kennedy","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Caitlin Kennedy","raw_affiliation_strings":["Purdue University"],"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060709469","display_name":"Shweta Jain","orcid":"https://orcid.org/0000-0002-2666-9058"},"institutions":[{"id":"https://openalex.org/I185103710","display_name":"University of California, Santa Cruz","ror":"https://ror.org/03s65by71","country_code":"US","type":"education","lineage":["https://openalex.org/I185103710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shweta Jain","raw_affiliation_strings":["University of California Santa Cruz"],"affiliations":[{"raw_affiliation_string":"University of California Santa Cruz","institution_ids":["https://openalex.org/I185103710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009319874","display_name":"Austin R. Benson","orcid":"https://orcid.org/0000-0001-6110-1583"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Austin R. Benson","raw_affiliation_strings":["Cornell University"],"affiliations":[{"raw_affiliation_string":"Cornell University","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084102378","display_name":"David F. Gleich","orcid":"https://orcid.org/0000-0002-8107-6474"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Gleich","raw_affiliation_strings":["Purdue University"],"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5065393424"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":0.5311,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.62081327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2927","last_page":"2933"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9898999929428101,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.7717195749282837},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7351402044296265},{"id":"https://openalex.org/keywords/graph-drawing","display_name":"Graph drawing","score":0.6507472991943359},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.6100088357925415},{"id":"https://openalex.org/keywords/graph-layout","display_name":"Graph Layout","score":0.60495924949646},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5363465547561646},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3604169487953186},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31949055194854736}],"concepts":[{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.7717195749282837},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7351402044296265},{"id":"https://openalex.org/C112953755","wikidata":"https://www.wikidata.org/wiki/Q739462","display_name":"Graph drawing","level":3,"score":0.6507472991943359},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.6100088357925415},{"id":"https://openalex.org/C2911174283","wikidata":"https://www.wikidata.org/wiki/Q739462","display_name":"Graph Layout","level":4,"score":0.60495924949646},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5363465547561646},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3604169487953186},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31949055194854736}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3366423.3380059","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380059","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380059","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380059","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W108936587","https://openalex.org/W1512522810","https://openalex.org/W1560267311","https://openalex.org/W1980431326","https://openalex.org/W1994727615","https://openalex.org/W2023098599","https://openalex.org/W2047443612","https://openalex.org/W2068015060","https://openalex.org/W2082773934","https://openalex.org/W2096826541","https://openalex.org/W2111708605","https://openalex.org/W2115043726","https://openalex.org/W2125910575","https://openalex.org/W2128965269","https://openalex.org/W2140635350","https://openalex.org/W2187089797","https://openalex.org/W2470861207","https://openalex.org/W2550802162","https://openalex.org/W2732288815","https://openalex.org/W2809156873","https://openalex.org/W2891939828","https://openalex.org/W2943373497","https://openalex.org/W2957077781","https://openalex.org/W2962756421","https://openalex.org/W3125564681","https://openalex.org/W4206275846","https://openalex.org/W4210535201"],"related_works":["https://openalex.org/W2346931493","https://openalex.org/W2031908202","https://openalex.org/W2766563406","https://openalex.org/W3160329999","https://openalex.org/W2026729066","https://openalex.org/W2895526338","https://openalex.org/W2903996962","https://openalex.org/W1555430809","https://openalex.org/W2294734161","https://openalex.org/W4388097011"],"abstract_inverted_index":{"In":[0],"the":[1,7,42,63,78,134,192],"simplest":[2],"setting,":[3],"graph":[4,59,71,75,151,172],"visualization":[5,60,112,152,173],"is":[6,36,84,215],"problem":[8],"of":[9,13,81,96,100,102,160,171,200,207],"producing":[10],"a":[11,27,33,111,114,120,124,147,169,184],"set":[12],"two-dimensional":[14],"coordinates":[15],"for":[16],"each":[17],"node":[18],"that":[19,65,139,178,186],"meaningfully":[20],"shows":[21],"connections":[22],"and":[23,52,175],"latent":[24],"structure":[25,132],"in":[26,62,113,133],"graph.":[28,135],"Among":[29],"other":[30],"uses,":[31],"having":[32],"meaningful":[34,94,115,158],"layout":[35,121],"often":[37,86,106],"useful":[38],"to":[39,92,109,146,156,190,211],"help":[40],"interpret":[41],"results":[43,214],"from":[44],"network":[45],"science":[46],"tasks":[47],"such":[48],"as":[49],"community":[50],"detection":[51],"link":[53],"prediction.":[54],"There":[55],"are":[56,66],"several":[57],"existing":[58,181],"techniques":[61,182],"literature":[64],"based":[67,143,150],"on":[68,144,183],"spectral":[69],"methods,":[70,82],"embeddings,":[72],"or":[73,88,118],"optimizing":[74],"distances.":[76],"Despite":[77],"large":[79,161,208],"number":[80,170],"it":[83,155,179],"still":[85],"challenging":[87],"extremely":[89],"time":[90,116],"consuming":[91],"produce":[93,110,119,157],"layouts":[95,206],"graphs":[97],"with":[98],"hundreds":[99],"thousands":[101],"vertices.":[103],"Existing":[104],"methods":[105],"either":[107],"fail":[108],"window,":[117],"colorfully":[122],"called":[123],"\u201chairball\u201d,":[125],"which":[126],"does":[127],"not":[128],"illustrate":[129],"any":[130],"internal":[131],"Here,":[136],"we":[137,176,196],"show":[138,197],"adding":[140],"higher-order":[141],"information":[142],"cliques":[145],"classic":[148],"eigenvector":[149],"technique":[153],"enables":[154],"plots":[159],"graphs.":[162],"We":[163],"further":[164],"evaluate":[165],"these":[166],"visualizations":[167],"along":[168],"metrics":[174],"find":[177],"outperforms":[180],"metric":[185],"uses":[187],"random":[188],"walks":[189],"measure":[191],"local":[193],"structure.":[194],"Finally,":[195],"many":[198],"examples":[199],"how":[201],"our":[202,213],"algorithm":[203],"successfully":[204],"produces":[205],"networks.":[209],"Code":[210],"reproduce":[212],"available.":[216]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
