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Scalable Graph Representation Learning via Locality-Sensitive Hashing

Proceedings of the 31st ACM International Conference on Information & Knowledge Management(2022)10.1145/3511808.3557689Source: DataRank Database

Scalable Graph Representation Learning via Locality-Sensitive Hashing is a research paper published in Proceedings of the 31st ACM International Conference on Information & Knowledge Management (2022). On theSindex it has a DataRank of 0. It has been cited 4 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access4 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 0.67)OA: hybridTopics (16)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Jyun‐Yu Jiang ORCID,Wei Wang ORCID,Xiusi Chen ORCID

Live enrichment

PubMed:21097893 OpenAlex:https://openalex.org/W4306317352

NIH (National Institutes of Health)

Grant: R35-HL135772

NSF (National Science Foundation)

Grant: 1829071, 2106859, 2119643

NIBIB

Grant: R01-EB027650

FWCI

0.67

Citation Percentile

0.7%

Citation Trend

2023

2025

hybridLicense: cc-by

conferencehttps://dl.acm.org/doi/pdf/10.1145/3511808.3557689

publisherhttps://dl.acm.org/doi/pdf/10.1145/3511808.3557689

publisherhttps://dl.acm.org/doi/10.1145/3511808.3557689

conferencehttps://doi.org/10.1145/3511808.3557689

Fields of Study

Advanced Graph Neural NetworksAdvanced Image and Video Retrieval TechniquesCaching and Content Delivery

Keywords

Locality-sensitive hashingComputer scienceScalabilityTheoretical computer scienceFeature learningGraphHash functionLeverage (statistics)Data miningMachine learningArtificial intelligenceHash tableDatabase

Sustainable Development Goals

SDG 0: Industry, innovation and infrastructure