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VERtex Similarity Embeddings

VERSE

40 papers tagged archive 2025-07-28

Introduced by Anton Tsitsulin et al. in VERSE: Versatile Graph Embeddings from Similarity Measures

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

VERtex Similarity Embeddings (VERSE) is a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network.

Source: Tsitsulin et al.

Image source: Tsitsulin et al.

PaperSource

Papers archive 2025-07-28

30 shown of 40, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 68 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Segmentation6
Link Prediction4
Semantic Segmentation4
Anatomy3
Computed Tomography (CT)3
Node Classification3
Translation3
Benchmarking2
Data Augmentation2
Decision Making2
Decoder2
General Classification2
Image Segmentation2
Instance Segmentation2
Language Modeling2
Language Modelling2
Music Generation2
Representation Learning2
Retrieval2
Sentence2

Usage over time archive 2025-07-28

Papers per year tagged with VERSE: 2018 to 2025, peak 8 8 0 2018: 2 papers 2018 2019: 7 papers 2019 2020: 6 papers 2020 2021: 4 papers 2021 2022: 6 papers 2022 2023: 8 papers 2023 2024: 5 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (40 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Graph Embeddings

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