> ML_ALGORITHM // GRAPH-ATTENTION-NETWORKS-GAT_v1.0

Graph Attention Networks (GAT & GATv2)

Graph neural network architecture leveraging multi-head self-attention mechanisms to assign anisotropic importance weights to neighbor edges during aggregation.

Spatial Graph Neural Networksgraph-relationalhigh-intrinsicmedium (1k-100k)

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Computational Complexity

Training Complexity:O(heads * (|V| * d^2 + |E| * d))

Inference Complexity:O(heads * |E| * d)

Hardware Profile

CPU Friendly:No

Requires GPU:Yes

Memory Footprint:high

Interpretability & Data

Interpretability Tier:high-intrinsic

Training Data Needs:medium (1k-100k)

Interpretability Assessment

Learned edge attention weights directly quantify which topological connections dominate node predictions.

Suitable Tasks & Supported Modalities

Suitable Tasks:

node classificationlink prediction

Supported Modalities:

graph

Implementing Libraries

torch-geometric

Deep Graph Library (DGL)DMLC / AWS / NYU · v2.2.1

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Foundational Literature

Graph Attention Networks (GAT)Petar Veličković, Guillem Cucurull (2018) · International Conference on Learning Representations (ICLR)

Common Pitfalls & Warnings

Standard GAT computes static attention where node ranking is independent of query node (resolved by GATv2 dynamic attention)