Methods › Graphs › Graph Embeddings › DAGNN

Directed Acyclic Graph Neural Network

DAGNN

6 papers tagged archive 2025-07-28

Introduced by Veronika Thost et al. in Directed Acyclic Graph Neural Networks

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

A GNN for dags, which injects their topological order as an inductive bias via asynchronous message passing.

PaperSource

Papers archive 2025-07-28

6 shown of 6, 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

10 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
Benchmarking2
Graph Neural Network2
Node Classification2
Reinforcement Learning (RL)2
Scheduling2
Deep Reinforcement Learning1
Graph Property Prediction1
Inductive Bias1
Model Selection1
Reinforcement Learning1

Usage over time archive 2025-07-28

Papers per year tagged with DAGNN: 2018 to 2023, peak 3 3 0 2018: 1 paper 2018 2019: 1 paper 2019 2020: 0 papers 2020 2021: 3 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (6 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

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections