{"id":"https://openalex.org/W2964560534","doi":"https://doi.org/10.1145/3292500.3330992","title":"Latent Network Summarization","display_name":"Latent Network Summarization","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2964560534","doi":"https://doi.org/10.1145/3292500.3330992","mag":"2964560534"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330992","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330992","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330992","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":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330992","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012455357","display_name":"Di Jin","orcid":"https://orcid.org/0000-0002-7445-9936"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Di Jin","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009957887","display_name":"Ryan A. Rossi","orcid":"https://orcid.org/0000-0001-9758-0635"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ryan A. Rossi","raw_affiliation_strings":["Adobe Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Adobe Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071007779","display_name":"Eunyee Koh","orcid":"https://orcid.org/0000-0003-2091-5972"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eunyee Koh","raw_affiliation_strings":["Adobe Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Adobe Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100718934","display_name":"Sungchul Kim","orcid":"https://orcid.org/0000-0003-3580-5290"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sungchul Kim","raw_affiliation_strings":["Adobe Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Adobe Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034026499","display_name":"Anup Rao","orcid":"https://orcid.org/0009-0004-7226-7860"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anup Rao","raw_affiliation_strings":["Adobe Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Adobe Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015996266","display_name":"Danai Koutra","orcid":"https://orcid.org/0000-0002-3206-8179"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danai Koutra","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5012455357"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":2.6013,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.92155511,"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":"987","last_page":"997"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994000196456909,"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/T10028","display_name":"Topic Modeling","score":0.9896000027656555,"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/automatic-summarization","display_name":"Automatic summarization","score":0.8885406255722046},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7244008183479309},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5586192607879639},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5013220310211182},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.45471832156181335},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37717387080192566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3275229334831238}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.8885406255722046},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7244008183479309},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5586192607879639},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5013220310211182},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.45471832156181335},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37717387080192566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3275229334831238}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330992","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330992","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330992","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"}],"best_oa_location":{"id":"doi:10.1145/3292500.3330992","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330992","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330992","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/G4496747893","display_name":null,"funder_award_id":"IS 1845491","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6595624021","display_name":null,"funder_award_id":"IIS 1845491","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7903790144","display_name":null,"funder_award_id":"1845491","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/W2964560534.pdf","grobid_xml":"https://content.openalex.org/works/W2964560534.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W348531675","https://openalex.org/W1888005072","https://openalex.org/W1904633530","https://openalex.org/W1964669181","https://openalex.org/W1979750072","https://openalex.org/W2021969955","https://openalex.org/W2057685268","https://openalex.org/W2090891622","https://openalex.org/W2108781142","https://openalex.org/W2119004437","https://openalex.org/W2125031621","https://openalex.org/W2131681506","https://openalex.org/W2154851992","https://openalex.org/W2168627253","https://openalex.org/W2294347342","https://openalex.org/W2374597300","https://openalex.org/W2415243320","https://openalex.org/W2519887557","https://openalex.org/W2521135474","https://openalex.org/W2607500032","https://openalex.org/W2612872092","https://openalex.org/W2624431344","https://openalex.org/W2743104969","https://openalex.org/W2774089819","https://openalex.org/W2785951678","https://openalex.org/W2798932700","https://openalex.org/W2888657195","https://openalex.org/W2962756421","https://openalex.org/W2962936633","https://openalex.org/W2963844113","https://openalex.org/W2964282455","https://openalex.org/W3099768174","https://openalex.org/W3101290021","https://openalex.org/W3103296165","https://openalex.org/W3103995645","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W4291474301","https://openalex.org/W4296410177"],"related_works":["https://openalex.org/W1606722271","https://openalex.org/W4287763734","https://openalex.org/W3035116611","https://openalex.org/W3141997814","https://openalex.org/W4226361842","https://openalex.org/W2923818335","https://openalex.org/W4310879833","https://openalex.org/W3044354590","https://openalex.org/W3044158376","https://openalex.org/W3149439221"],"abstract_inverted_index":{"Motivated":[0],"by":[1],"the":[2,13,28,37,47,54,81,102,161,171],"computational":[3],"and":[4,43,73,85,113,118,168,176],"storage":[5,147],"challenges":[6],"that":[7,19,33,66],"dense":[8],"embeddings":[9],"pose,":[10],"we":[11,159],"introduce":[12],"problem":[14],"of":[15,27,36,70,77,83,163],"latent":[16,25,62,107],"network":[17,63,108],"summarization":[18,64],"aims":[20],"to":[21,49,79],"learn":[22],"a":[23,68],"compact,":[24],"representation":[26],"graph":[29,39,175],"structure":[30,82,90],"with":[31,101,121],"dimensionality":[32],"is":[34,91,111],"independent":[35],"input":[38],"size":[40],"(\\i.e.,":[41],"#nodes":[42],"#edges),":[44],"while":[45,142],"retaining":[46],"ability":[48],"derive":[50],"node":[51],"representations":[52],"on":[53,131,153],"fly.":[55],"We":[56],"propose":[57],"Multi-LENS,":[58],"an":[59],"inductive":[60],"multi-level":[61],"approach":[65],"leverages":[67],"set":[69],"relational":[71,74,103],"operators":[72],"functions":[75,104],"(compositions":[76],"operators)":[78],"capture":[80],"egonets":[84],"higher-order":[86],"subgraphs,":[87],"respectively.":[88],"The":[89],"stored":[92],"in":[93,137,165,170],"low-rank,":[94],"size-independent":[95],"structural":[96],"feature":[97],"matrices,":[98],"which":[99],"along":[100],"comprise":[105],"our":[106],"summary.":[109],"Multi-LENS":[110,164],"general":[112],"naturally":[114],"supports":[115],"both":[116],"homogeneous":[117],"heterogeneous":[119],"graphs":[120,133],"or":[122,127],"without":[123],"directionality,":[124],"weights,":[125],"attributes":[126],"labels.":[128],"Extensive":[129],"experiments":[130],"real":[132],"show":[134,160],"3.5-34.3%":[135],"improvement":[136],"AUC":[138],"for":[139],"link":[140],"prediction,":[141],"requiring":[143],"80-2152x":[144],"less":[145],"output":[146],"space":[148],"than":[149],"baseline":[150],"embedding":[151],"methods":[152],"large":[154],"datasets.":[155],"As":[156],"application":[157],"areas,":[158],"effectiveness":[162],"detecting":[166],"anomalies":[167],"events":[169],"Enron":[172],"email":[173],"communication":[174],"Twitter":[177],"co-mention":[178],"graph.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":3}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}