{"id":"https://openalex.org/W7165640461","doi":"https://doi.org/10.48550/arxiv.2606.22180","title":"FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism","display_name":"FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism","publication_year":2026,"publication_date":"2026-06-20","ids":{"openalex":"https://openalex.org/W7165640461","doi":"https://doi.org/10.48550/arxiv.2606.22180"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22180","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22180","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":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.2606.22180","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100601526","display_name":"Peng Fang","orcid":"https://orcid.org/0000-0002-6495-7317"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139187638","display_name":"Arijit Khan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khan, Arijit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139216193","display_name":"Ziqiang Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Ziqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004937453","display_name":"Zhenli Li","orcid":"https://orcid.org/0000-0002-3682-164X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhenli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139199410","display_name":"Yibo Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yibo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029787563","display_name":"Fang Wang","orcid":"https://orcid.org/0000-0002-3327-4177"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Fang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5064771930","display_name":"Feng Dan","orcid":"https://orcid.org/0000-0002-8360-2415"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Dan","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":0.6305000185966492,"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":0.6305000185966492,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.3330000042915344,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.003800000064074993,"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/scalability","display_name":"Scalability","score":0.7874000072479248},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6269000172615051},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5138000249862671},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.49140000343322754},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4772999882698059},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4535999894142151},{"id":"https://openalex.org/keywords/pci-express","display_name":"PCI Express","score":0.3734000027179718},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.35409998893737793}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8184000253677368},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7874000072479248},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6269000172615051},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.567799985408783},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5138000249862671},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.49140000343322754},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4772999882698059},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4611000120639801},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C64270927","wikidata":"https://www.wikidata.org/wiki/Q206924","display_name":"PCI Express","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.35409998893737793},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C2779172887","wikidata":"https://www.wikidata.org/wiki/Q184316","display_name":"PageRank","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.27000001072883606},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.26669999957084656},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22180","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22180","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.22180","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22180","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph":[0],"embedding":[1,32,62],"maps":[2],"graph":[3,31,104],"nodes":[4,51,116],"into":[5],"low-dimensional":[6],"vectors":[7],"to":[8,24,82,118,140,151,168],"support":[9],"applications":[10],"such":[11,78],"as":[12],"recommendation,":[13],"fraud":[14],"detection,":[15],"and":[16,29,52,87,111,126,145,196],"graph-based":[17],"retrieval-augmented":[18],"generation":[19],"(GraphRAG).":[20],"As":[21],"graphs":[22,179],"scale":[23],"billions":[25],"of":[26,67,187],"edges,":[27],"scalable":[28,102],"efficient":[30],"has":[33],"become":[34],"increasingly":[35],"important.":[36],"Existing":[37],"frameworks":[38],"commonly":[39],"adopt":[40],"a":[41,97,158],"sampling-training":[42],"paradigm,":[43],"in":[44,91],"which":[45],"mini-batches":[46],"are":[47],"constructed":[48],"by":[49,192],"sampling":[50,56,110,164],"their":[53],"neighbors.":[54],"However,":[55],"is":[57],"typically":[58],"decoupled":[59],"from":[60],"evolving":[61],"quality,":[63],"causing":[64],"redundant":[65,124],"exploration":[66],"well-trained":[68],"regions":[69],"while":[70],"under-sampling":[71],"undertrained":[72,115],"nodes.":[73],"At":[74],"the":[75],"system":[76,100],"level,":[77],"decoupling":[79],"further":[80],"leads":[81],"excessive":[83],"communication,":[84],"serialized":[85],"execution,":[86],"low":[88],"resource":[89],"utilization":[90],"distributed":[92,103],"environments.":[93],"We":[94],"present":[95],"FeLoG,":[96],"feedback":[98],"loop-driven":[99],"for":[101],"embedding.":[105],"(1)":[106],"FeLoG":[107,182],"introduces":[108],"feedback-coupled":[109],"training,":[112],"dynamically":[113],"prioritizing":[114],"according":[117],"real-time":[119],"embedding-quality":[120],"feedback,":[121],"thereby":[122],"reducing":[123],"computation":[125],"accelerating":[127],"convergence.":[128],"(2)":[129],"It":[130,156],"employs":[131],"activity-aware":[132],"communication":[133,190],"that":[134,161,181],"compresses":[135],"frequently":[136,148],"occurring":[137],"node":[138],"sequences":[139],"reduce":[141,152],"intra-machine":[142],"PCIe":[143],"traffic":[144],"selectively":[146],"synchronizes":[147],"updated":[149],"embeddings":[150],"inter-machine":[153],"communication.":[154],"(3)":[155],"adopts":[157],"round-interleaved":[159],"pipeline":[160],"overlaps":[162],"next-round":[163],"with":[165],"current-round":[166],"training":[167],"improve":[169],"CPU-GPU":[170,200],"utilization.":[171,201],"Experiments":[172],"against":[173],"six":[174],"state-of-the-art":[175],"baselines":[176],"on":[177],"large-scale":[178],"show":[180],"achieves":[183],"an":[184],"average":[185],"speedup":[186],"27.9x,":[188],"reduces":[189],"cost":[191],"more":[193],"than":[194],"53.1%,":[195],"sustains":[197],"over":[198],"80%":[199]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}
