{"id":"https://openalex.org/W7164553630","doi":"https://doi.org/10.48550/arxiv.2606.12581","title":"Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark","display_name":"Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164553630","doi":"https://doi.org/10.48550/arxiv.2606.12581"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.12581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12581","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.12581","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124969980","display_name":"Mateusz Stolarski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stolarski, Mateusz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138492827","display_name":"Micha\u0142 Czuba","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Czuba, Micha\u0142","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039269881","display_name":"Piotr Bielak","orcid":"https://orcid.org/0000-0002-1487-2569"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bielak, Piotr","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138506026","display_name":"Piotr Br\u00f3dka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Br\u00f3dka, Piotr","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.95169997215271,"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":0.95169997215271,"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":0.009800000116229057,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.004900000058114529,"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/graph-reduction","display_name":"Graph reduction","score":0.7019000053405762},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.6796000003814697},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6484000086784363},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5379999876022339},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44510000944137573},{"id":"https://openalex.org/keywords/power-graph-analysis","display_name":"Power graph analysis","score":0.4138999879360199},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4106000065803528},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.38100001215934753}],"concepts":[{"id":"https://openalex.org/C97042676","wikidata":"https://www.wikidata.org/wiki/Q5597097","display_name":"Graph reduction","level":3,"score":0.7019000053405762},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.6796000003814697},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6484000086784363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6412000060081482},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5379999876022339},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44510000944137573},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.4138999879360199},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39730000495910645},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37770000100135803},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3598000109195709},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3506999909877777},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28850001096725464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.12581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12581","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.12581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12581","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real-world":[0],"networks":[1,173],"are":[2],"inherently":[3],"incomplete,":[4],"noisy,":[5],"and":[6,16,44,90,93,130,155],"dynamically":[7],"evolving,":[8],"making":[9],"it":[10],"difficult":[11],"to":[12,114],"capture":[13],"all":[14],"actors":[15],"their":[17],"relationships.":[18],"Their":[19],"scale":[20],"often":[21],"renders":[22],"direct":[23],"analysis":[24],"computationally":[25],"demanding.":[26],"While":[27],"influence":[28],"maximisation":[29],"(IM)":[30],"has":[31],"been":[32],"widely":[33],"studied,":[34],"the":[35,57,100,106,126,140,149,156,185],"role":[36],"of":[37,85,128,142,179,187],"graph":[38,96,117],"reduction":[39,97,118,143,180],"as":[40],"a":[41,82],"preprocessing":[42],"step,":[43],"its":[45],"impact":[46,141],"on":[47,81,147,167],"IM":[48,69,110,134],"accuracy,":[49],"remains":[50],"underexplored.":[51],"In":[52],"this":[53],"work,":[54],"we":[55,124],"introduce":[56],"Spreading-Oriented":[58],"Reduction":[59],"Benchmark":[60],"(SORB),":[61],"an":[62,77],"open-source,":[63],"standardised":[64],"framework":[65],"for":[66,95],"systematically":[67],"evaluating":[68],"models":[70],"across":[71,132],"diverse":[72],"task":[73,158],"settings.":[74],"SORB":[75],"provides":[76],"extensible":[78],"pipeline":[79],"operating":[80],"representative":[83],"collection":[84],"real-world":[86],"networks,":[87,169],"including":[88],"single-":[89],"multilayer":[91,172],"structures,":[92],"accounts":[94],"directly":[98],"into":[99],"evaluation":[101,190],"process.":[102],"This":[103],"design":[104],"shifts":[105],"focus":[107],"from":[108],"analysing":[109],"algorithms":[111],"in":[112,195],"isolation":[113],"quantifying":[115],"how":[116],"alters":[119],"predictive":[120],"performance.":[121],"Using":[122],"SORB,":[123],"study":[125],"effects":[127],"sparsification":[129,162],"coarsening":[131],"multiple":[133],"scenarios.":[135],"Our":[136],"results":[137],"show":[138],"that":[139],"is":[144],"strongly":[145],"dependent":[146],"both":[148],"network":[150],"type":[151],"(single-layer":[152],"vs.":[153,160],"multirelational)":[154],"downstream":[157],"($Gain@k$":[159],"$\\mathrm{AUC}_{\\mathrm{cutoff}}$):":[161],"preserves":[163],"seed":[164],"set":[165],"quality":[166],"single-layer":[168],"whereas":[170],"flattened":[171],"exhibit":[174],"systematic":[175],"ranking":[176],"degradation":[177],"regardless":[178],"strategy.":[181],"These":[182],"findings":[183],"highlight":[184],"importance":[186],"reduction-aware,":[188],"multi-task":[189],"when":[191],"studying":[192],"spreading":[193],"processes":[194],"complex":[196],"networks.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-13T00:00:00"}
