{"id":"https://openalex.org/W4317181464","doi":"https://doi.org/10.1007/978-3-031-21534-6_3","title":"Increasing the Sampling Efficiency for the Link Assessment Problem","display_name":"Increasing the Sampling Efficiency for the Link Assessment Problem","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4317181464","doi":"https://doi.org/10.1007/978-3-031-21534-6_3"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-031-21534-6_3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-21534-6_3","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-21534-6_3.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-21534-6_3.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073539369","display_name":"Andr\u00e9 Lucas Chinazzo","orcid":null},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Andr\u00e9 Chinazzo","raw_affiliation_strings":["TU Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"TU Kaiserslautern, Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068426047","display_name":"Christian De Schryver","orcid":"https://orcid.org/0000-0002-5911-5289"},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian De Schryver","raw_affiliation_strings":["TU Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"TU Kaiserslautern, Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018655950","display_name":"Katharina A. Zweig","orcid":"https://orcid.org/0000-0002-4294-9017"},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Katharina Zweig","raw_affiliation_strings":["TU Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"TU Kaiserslautern, Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059285190","display_name":"Norbert Wehn","orcid":"https://orcid.org/0000-0002-9010-086X"},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Norbert Wehn","raw_affiliation_strings":["TU Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"TU Kaiserslautern, Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5073539369"],"corresponding_institution_ids":["https://openalex.org/I153267046"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60854846,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"39","last_page":"56"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.996399998664856,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8409677743911743},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.8014304637908936},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.6445753574371338},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.539404034614563},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5350348353385925},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.44246381521224976},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4357404410839081},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.4171209931373596},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.412162721157074},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4093107283115387},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40098389983177185},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3813268840312958},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.35557886958122253},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3338581919670105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28836768865585327}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8409677743911743},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.8014304637908936},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.6445753574371338},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.539404034614563},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5350348353385925},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.44246381521224976},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4357404410839081},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.4171209931373596},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.412162721157074},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4093107283115387},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40098389983177185},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3813268840312958},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.35557886958122253},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3338581919670105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28836768865585327},{"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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-031-21534-6_3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-21534-6_3","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-21534-6_3.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-3-031-21534-6_3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-21534-6_3","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-21534-6_3.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4317181464.pdf"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W834693420","https://openalex.org/W1139185857","https://openalex.org/W1916020809","https://openalex.org/W1978036582","https://openalex.org/W1979104937","https://openalex.org/W1988398836","https://openalex.org/W2016786780","https://openalex.org/W2025605741","https://openalex.org/W2037705937","https://openalex.org/W2050194054","https://openalex.org/W2053906518","https://openalex.org/W2076248831","https://openalex.org/W2083045667","https://openalex.org/W2086688596","https://openalex.org/W2121821841","https://openalex.org/W2289974125","https://openalex.org/W2290279878","https://openalex.org/W2336610310","https://openalex.org/W2538732301","https://openalex.org/W2880603155","https://openalex.org/W2962843773","https://openalex.org/W2963979722","https://openalex.org/W2964035435","https://openalex.org/W4235019172","https://openalex.org/W4238452917","https://openalex.org/W6697553284"],"related_works":["https://openalex.org/W3177062893","https://openalex.org/W3125143773","https://openalex.org/W2007032764","https://openalex.org/W803550684","https://openalex.org/W2483226803","https://openalex.org/W4352977312","https://openalex.org/W3143937874","https://openalex.org/W4312926500","https://openalex.org/W2067280619","https://openalex.org/W4251343851"],"abstract_inverted_index":{"Abstract":[0],"Complex":[1],"graphs":[2],"are":[3,35,38],"at":[4],"the":[5,29,45,51,55,77,99,107,118,141],"heart":[6],"of":[7,31,48,101,109,174],"today\u2019s":[8],"big":[9],"data":[10],"challenges":[11],"like":[12],"recommendation":[13],"systems,":[14],"customer":[15],"behavior":[16],"modeling,":[17],"or":[18],"incident":[19],"detection":[20],"systems.":[21],"One":[22],"reoccurring":[23,39],"task":[24],"in":[25,54,66,83,125],"these":[26],"fields":[27],"is":[28,127],"extraction":[30],"network":[32,57],"motifs,":[33],"which":[34],"subgraphs":[36],"that":[37,97,116,139,148,164],"and":[40,137],"statistically":[41],"significant.":[42],"To":[43],"assess":[44],"statistical":[46],"significance":[47],"their":[49,63],"occurrence,":[50],"observed":[52],"values":[53],"real":[56],"need":[58],"to":[59,62],"be":[60,150],"compared":[61],"expected":[64],"value":[65],"a":[67,153,162,172],"random":[68],"graph":[69],"model.":[70],"In":[71],"this":[72],"chapter,":[73],"we":[74,91,160],"focus":[75],"on":[76],"so-called":[78,145],"Link":[79],"Assessment":[80],"(LA)":[81],"problem,":[82],"particular":[84],"for":[85,105,133,156],"bipartite":[86],"networks.":[87],"Lacking":[88],"closed-form":[89],"solutions,":[90],"require":[92],"stochastic":[93],"Monte":[94],"Carlo":[95],"approaches":[96],"raise":[98],"challenge":[100],"finding":[102],"appropriate":[103],"metrics":[104,136,143],"quantifying":[106],"quality":[108,126,135],"results":[110,132],"(QoR)":[111],"together":[112],"with":[113,168],"suitable":[114],"heuristics":[115],"stop":[117],"computation":[119],"process":[120],"if":[121],"no":[122],"further":[123],"increase":[124],"expected.":[128],"We":[129],"provide":[130],"investigation":[131],"three":[134],"show":[138],"observing":[140],"right":[142],"reveals":[144],"phase":[146],"transitions":[147],"can":[149],"used":[151],"as":[152],"reliable":[154],"basis":[155],"such":[157],"heuristics.":[158],"Finally,":[159],"propose":[161],"heuristic":[163],"has":[165],"been":[166],"evaluated":[167],"real-word":[169],"datasets,":[170],"providing":[171],"speedup":[173],"$$15.4\\times":[175],"$$":[176],"<mml:math":[177],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\">":[178],"<mml:mrow>":[179],"<mml:mn>15.4</mml:mn>":[180],"<mml:mo>\u00d7</mml:mo>":[181],"</mml:mrow>":[182],"</mml:math>":[183],"over":[184],"previous":[185],"approaches.":[186]},"counts_by_year":[],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}
