{"id":"https://openalex.org/W4391124731","doi":"https://doi.org/10.48550/arxiv.2401.10799","title":"Novel Representation Learning Technique using Graphs for Performance Analytics","display_name":"Novel Representation Learning Technique using Graphs for Performance Analytics","publication_year":2024,"publication_date":"2024-01-19","ids":{"openalex":"https://openalex.org/W4391124731","doi":"https://doi.org/10.48550/arxiv.2401.10799"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2401.10799","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.10799","pdf_url":"https://arxiv.org/pdf/2401.10799","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":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.10799","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058282095","display_name":"Tarek Ramadan","orcid":"https://orcid.org/0000-0002-6489-7668"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ramadan, Tarek","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093766677","display_name":"Ankur Lahiry","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lahiry, Ankur","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5002465410","display_name":"Tanzima Islam","orcid":"https://orcid.org/0000-0003-2877-5871"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Islam, Tanzima Z.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5058282095"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.9986000061035156,"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":0.9986000061035156,"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/T12127","display_name":"Software System Performance and Reliability","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11122","display_name":"Online Learning and Analytics","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/computer-science","display_name":"Computer science","score":0.8103218078613281},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7415385842323303},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.7004167437553406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5999093651771545},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5888819694519043},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5162272453308105},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5155755877494812},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5062960386276245},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48102980852127075},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.476682186126709},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38811129331588745},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.181878000497818}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8103218078613281},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7415385842323303},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.7004167437553406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5999093651771545},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5888819694519043},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5162272453308105},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5155755877494812},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5062960386276245},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48102980852127075},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.476682186126709},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38811129331588745},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.181878000497818}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2401.10799","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.10799","pdf_url":"https://arxiv.org/pdf/2401.10799","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":"","raw_type":null},{"id":"doi:10.48550/arxiv.2401.10799","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2401.10799","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2401.10799","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.10799","pdf_url":"https://arxiv.org/pdf/2401.10799","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":"","raw_type":null},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6925437540","display_name":null,"funder_award_id":"DE-SC0022843","funder_id":"https://openalex.org/F4320332359","funder_display_name":"Office of Science"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320310830","display_name":"Texas State University","ror":"https://ror.org/009ey6w22"},{"id":"https://openalex.org/F4320332359","display_name":"Office of Science","ror":"https://ror.org/00mmn6b08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391124731.pdf","grobid_xml":"https://content.openalex.org/works/W4391124731.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3199964822","https://openalex.org/W3048601286","https://openalex.org/W3034267371","https://openalex.org/W2965925734","https://openalex.org/W4232132981","https://openalex.org/W4238046985","https://openalex.org/W3164948662","https://openalex.org/W3003242282","https://openalex.org/W3153597579","https://openalex.org/W3012824888"],"abstract_inverted_index":{"The":[0],"performance":[1,85],"analytics":[2],"domain":[3],"in":[4,99,155,226],"High":[5],"Performance":[6],"Computing":[7],"(HPC)":[8],"uses":[9],"tabular":[10,33,84],"data":[11,86],"to":[12,89,109,126,185,198,221],"solve":[13],"regression":[14,182],"problems,":[15],"such":[16,114],"as":[17,115],"predicting":[18],"the":[19,28,37,50,53,91,118,142,150,153,156,160,163,230],"execution":[20],"time.":[21],"Existing":[22],"Machine":[23,238],"Learning":[24,239],"(ML)":[25],"techniques":[26,98],"leverage":[27,90],"correlations":[29],"among":[30],"features":[31,48,104,154],"given":[32],"datasets,":[34],"not":[35,121],"leveraging":[36],"relationships":[38,102],"between":[39,103,152],"samples":[40],"directly.":[41],"Moreover,":[42],"since":[43],"high-quality":[44],"embeddings":[45,165],"from":[46,166],"raw":[47],"improve":[49],"fidelity":[51],"of":[52,82,93,162],"downstream":[54],"predictive":[55],"models,":[56],"existing":[57],"methods":[58,136],"rely":[59],"on":[60,149,169],"extensive":[61],"feature":[62],"engineering":[63],"and":[64,69,105,141,212,218,237],"pre-processing":[65],"steps,":[66],"costing":[67],"time":[68],"manual":[70],"effort.":[71],"To":[72,129],"fill":[73],"these":[74],"two":[75],"gaps,":[76],"we":[77,124,133],"propose":[78,134],"a":[79,175],"novel":[80],"idea":[81],"transforming":[83],"into":[87],"graphs":[88],"advancement":[92],"Graph":[94],"Neural":[95,214],"Network-based":[96],"(GNN)":[97],"capturing":[100],"complex":[101],"samples.":[106,157],"In":[107],"contrast":[108],"other":[110,186],"ML":[111],"application":[112],"domains,":[113],"social":[116],"networks,":[117],"graph":[119,211],"is":[120],"given;":[122],"instead,":[123],"need":[125],"build":[127],"it.":[128],"address":[130],"this":[131],"gap,":[132],"graph-building":[135],"where":[137],"nodes":[138],"represent":[139],"samples,":[140],"edges":[143],"are":[144],"automatically":[145],"inferred":[146],"iteratively":[147],"based":[148,168],"similarity":[151],"We":[158],"evaluate":[159],"effectiveness":[161],"generated":[164],"GNNs":[167],"how":[170],"well":[171],"they":[172],"make":[173],"even":[174,195],"simple":[176],"feed-forward":[177],"neural":[178],"network":[179],"perform":[180],"for":[181,203,234],"tasks":[183],"compared":[184],"state-of-the-art":[187],"representation":[188],"learning":[189],"techniques.":[190],"Our":[191],"evaluation":[192],"demonstrates":[193],"that":[194],"with":[196],"up":[197,220],"25%":[199],"random":[200],"missing":[201],"values":[202],"each":[204],"dataset,":[205],"our":[206],"method":[207],"outperforms":[208],"commonly":[209],"used":[210],"Deep":[213],"Network":[215],"(DNN)-based":[216],"approaches":[217],"achieves":[219],"61.67%":[222],"&amp;":[223],"78.56%":[224],"improvement":[225],"MSE":[227],"loss":[228],"over":[229],"DNN":[231],"baseline":[232],"respectively":[233],"HPC":[235],"dataset":[236],"Datasets.":[240]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
