Methods › Graphs › Graph Representation Learning › AWARE

Attentive Walk-Aggregating Graph Neural Network

AWARE

1,883 papers tagged archive 2025-07-28

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

We propose to theoretically and empirically examine the effect of incorporating weighting schemes into walk-aggregating GNNs. To this end, we propose a simple, interpretable, and end-to-end supervised GNN model, called AWARE (Attentive Walk-Aggregating GRaph Neural NEtwork), for graph-level prediction. AWARE aggregates the walk information by means of weighting schemes at distinct levels (vertex-, walk-, and graph-level) in a principled manner. By virtue of the incorporated weighting schemes at these different levels, AWARE can emphasize the information important for prediction while diminishing the irrelevant ones—leading to representations that can improve learning performance.

Papers archive 2025-07-28

30 shown of 1,883, 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 923 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
Semantic Segmentation81
Language Modelling72
Representation Learning69
Quantization67
Segmentation64
Object61
Retrieval61
Decoder56
Language Modeling55
Object Detection49
object-detection49
Decision Making47
Prediction41
Autonomous Driving39
Sentence39
Contrastive Learning38
Federated Learning38
Question Answering38
reinforcement-learning36
Diversity35

Usage over time archive 2025-07-28

Papers per year tagged with AWARE: 2020 to 2025, peak 452 452 0 2020: 57 papers 2020 2021: 323 papers 2021 2022: 374 papers 2022 2023: 452 papers 2023 2024: 442 papers 2024 2025: 235 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (1,883 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 Representation Learning

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