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Stochastic Steady-state Embedding

SSE

41 papers tagged archive 2025-07-28

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

Stochastic Steady-state Embedding (SSE) is an algorithm that can learn many steady-state algorithms over graphs. Different from graph neural network family models, SSE is trained stochastically which only requires 1-hop information, but can capture fixed point relationships efficiently and effectively.

Description and Image from: Learning Steady-States of Iterative Algorithms over Graphs

Source: Learning Steady-States of Iterative Algorithms over Graphs

Papers archive 2025-07-28

30 shown of 41, 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 36 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
Time Series4
Clustering3
Prediction2
Q-Learning2
Reinforcement Learning (RL)2
State Estimation2
Stock Price Prediction2
Time Series Analysis2
Active Learning1
Adversarial Attack1
Binarization1
CPU1
Collaborative Filtering1
Collaborative Ranking1
Contrastive Learning1
Decision Making1
Decoder1
Deep Reinforcement Learning1
Distributed Computing1
Fairness1

Usage over time archive 2025-07-28

Papers per year tagged with SSE: 2018 to 2025, peak 8 8 0 2018: 3 papers 2018 2019: 4 papers 2019 2020: 5 papers 2020 2021: 6 papers 2021 2022: 7 papers 2022 2023: 3 papers 2023 2024: 8 papers 2024 2025: 5 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (41 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 Models

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