{"id":"https://openalex.org/W2946144513","doi":"https://doi.org/10.1145/3292500.3330891","title":"Network Density of States","display_name":"Network Density of States","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2946144513","doi":"https://doi.org/10.1145/3292500.3330891","mag":"2946144513"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330891","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330891","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330891","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330891","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Kun Dong","orcid":null},"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":true,"raw_author_name":"Kun Dong","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Austin R. Benson","orcid":null},"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, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":null,"display_name":"David Bindel","orcid":null},"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":"David Bindel","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.8638,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.73161618,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1152","last_page":"1161"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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/T11476","display_name":"Graph theory and applications","score":0.9932000041007996,"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"}},{"id":"https://openalex.org/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.991599977016449,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.7286999821662903},{"id":"https://openalex.org/keywords/spectral-graph-theory","display_name":"Spectral graph theory","score":0.6222000122070312},{"id":"https://openalex.org/keywords/graph-energy","display_name":"Graph energy","score":0.5408999919891357},{"id":"https://openalex.org/keywords/centrality","display_name":"Centrality","score":0.5378000140190125},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4894999861717224},{"id":"https://openalex.org/keywords/spectral-line","display_name":"Spectral line","score":0.44020000100135803},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.43689998984336853},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4366999864578247},{"id":"https://openalex.org/keywords/spectral-theory","display_name":"Spectral theory","score":0.40849998593330383},{"id":"https://openalex.org/keywords/maximum-entropy-spectral-estimation","display_name":"Maximum entropy spectral estimation","score":0.39809998869895935}],"concepts":[{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.7286999821662903},{"id":"https://openalex.org/C74003402","wikidata":"https://www.wikidata.org/wiki/Q3180727","display_name":"Spectral graph theory","level":5,"score":0.6222000122070312},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.597100019454956},{"id":"https://openalex.org/C78913703","wikidata":"https://www.wikidata.org/wiki/Q5597087","display_name":"Graph energy","level":5,"score":0.5408999919891357},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4894999861717224},{"id":"https://openalex.org/C4839761","wikidata":"https://www.wikidata.org/wiki/Q212111","display_name":"Spectral line","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4366999864578247},{"id":"https://openalex.org/C98306654","wikidata":"https://www.wikidata.org/wiki/Q2409122","display_name":"Spectral theory","level":3,"score":0.40849998593330383},{"id":"https://openalex.org/C89735579","wikidata":"https://www.wikidata.org/wiki/Q6795894","display_name":"Maximum entropy spectral estimation","level":3,"score":0.39809998869895935},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C204710682","wikidata":"https://www.wikidata.org/wiki/Q3036983","display_name":"Integral graph","level":5,"score":0.39309999346733093},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C23463724","wikidata":"https://www.wikidata.org/wiki/Q2308831","display_name":"Spectral method","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.3368000090122223},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.33649998903274536},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.33340001106262207},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C49777392","wikidata":"https://www.wikidata.org/wiki/Q5535495","display_name":"Geometric networks","level":3,"score":0.3140000104904175},{"id":"https://openalex.org/C2983668108","wikidata":"https://www.wikidata.org/wiki/Q280453","display_name":"Spectral analysis","level":3,"score":0.31349998712539673},{"id":"https://openalex.org/C94360122","wikidata":"https://www.wikidata.org/wiki/Q1967557","display_name":"Spectral power distribution","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C76444178","wikidata":"https://www.wikidata.org/wiki/Q72897900","display_name":"Connectivity","level":3,"score":0.3102000057697296},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.29919999837875366},{"id":"https://openalex.org/C30609935","wikidata":"https://www.wikidata.org/wiki/Q7291969","display_name":"Random geometric graph","level":5,"score":0.2955000102519989},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.2858999967575073},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C2985906921","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Spectral properties","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C51364350","wikidata":"https://www.wikidata.org/wiki/Q3123529","display_name":"Spectral geometry","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C64339825","wikidata":"https://www.wikidata.org/wiki/Q722659","display_name":"Graph property","level":5,"score":0.27219998836517334},{"id":"https://openalex.org/C128803854","wikidata":"https://www.wikidata.org/wiki/Q1307821","display_name":"Eigenfunction","level":3,"score":0.2678999900817871},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3292500.3330891","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330891","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330891","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1905.09758","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.09758","pdf_url":"https://arxiv.org/pdf/1905.09758","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":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3292500.3330891","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330891","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330891","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2037625258","display_name":"Computing Spectral Distributions for Graph Analysis and Classification","funder_award_id":"1620038","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5743040065","display_name":null,"funder_award_id":"DMS-1620038","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":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2946144513.pdf","grobid_xml":"https://content.openalex.org/works/W2946144513.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W638149299","https://openalex.org/W1973391382","https://openalex.org/W1984159596","https://openalex.org/W1985339741","https://openalex.org/W1990457366","https://openalex.org/W2006332263","https://openalex.org/W2006340475","https://openalex.org/W2012123976","https://openalex.org/W2027199101","https://openalex.org/W2031481696","https://openalex.org/W2050210602","https://openalex.org/W2053037364","https://openalex.org/W2057450058","https://openalex.org/W2072211488","https://openalex.org/W2075984423","https://openalex.org/W2108214478","https://openalex.org/W2112976607","https://openalex.org/W2114030927","https://openalex.org/W2140802988","https://openalex.org/W2744808284","https://openalex.org/W2963225255","https://openalex.org/W3009233884","https://openalex.org/W4205463258","https://openalex.org/W4211197802"],"related_works":[],"abstract_inverted_index":{"Spectral":[0],"analysis":[1],"connects":[2],"graph":[3,16,43,85,137,196],"structure":[4,197],"to":[5,41,82,99,102,130,191],"the":[6,23,29,36,63,68,74,90,110,132,174,180,203],"eigenvalues":[7,55,72],"and":[8,56,101,126,187],"eigenvectors":[9],"of":[10,14,25,31,65,71,89,92,112,115,135,169,176,182],"associated":[11,32,58],"matrices.":[12],"Much":[13],"spectral":[15,21,39,42,76,113,133,150,177,189],"theory":[17,44],"descends":[18],"directly":[19],"from":[20,38,202],"geometry,":[22,62],"study":[24,64],"differentiable":[26],"manifolds":[27],"through":[28,67],"spectra":[30],"differential":[33],"operators.":[34],"But":[35],"translation":[37],"geometry":[40],"has":[45],"largely":[46,80,96],"focused":[47],"on":[48,159],"results":[49],"involving":[50],"only":[51],"a":[52,156,160],"few":[53],"extreme":[54],"their":[57],"eigenvalues.":[59],"Unlike":[60],"in":[61,122],"graphs":[66,94,153],"overall":[69],"distribution":[70],"---":[73,78],"\\em":[75],"density":[77],"is":[79],"limited":[81],"simple":[83],"random":[84],"models.":[86],"The":[87,139],"interior":[88],"spectrum":[91],"real-world":[93,116],"remains":[95],"unexplored,":[97],"difficult":[98],"compute":[100,162],"interpret.":[103],"In":[104],"this":[105],"paper,":[106],"we":[107,146,171],"delve":[108],"into":[109],"heart":[111],"densities":[114,151,178,190],"graphs.":[117],"We":[118],"borrow":[119],"tools":[120],"developed":[121],"condensed":[123],"matter":[124],"physics,":[125],"add":[127],"novel":[128],"adaptations":[129],"handle":[131],"signatures":[134],"common":[136,184],"motifs.":[138],"resulting":[140],"methods":[141],"are":[142],"highly":[143],"efficient,":[144],"as":[145],"illustrate":[147],"by":[148],"computing":[149],"for":[152],"with":[154],"over":[155],"billion":[157],"edges":[158],"single":[161],"node.":[163],"Beyond":[164],"providing":[165],"visually":[166],"compelling":[167],"fingerprints":[168],"graphs,":[170],"show":[172],"how":[173],"estimation":[175],"facilitates":[179],"computation":[181],"many":[183],"centrality":[185],"measures,":[186],"use":[188],"estimate":[192],"meaningful":[193],"information":[194],"about":[195],"that":[198],"cannot":[199],"be":[200],"inferred":[201],"extremal":[204],"eigenpairs":[205],"alone.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2019-05-29T00:00:00"}