A Temporally Augmented Graph Attention Network for Affordance Classification
Graph attention networks (GATs) provide one of the best frameworks for learning node representations in relational data; but, existing variants such as Graph Attention Network (GAT) mainly operate on static graphs and rely on implicit temporal aggregation when applied to sequential data. In this paper, we introduce Electroencephalography-temporal Graph Attention Network (EEG-tGAT), a temporally augmented formulation of GATv2 that is tailored for affordance classification from interaction sequences. The proposed model incorporates temporal attention to modulate the contribution of different time segments and temporal dropout to regularize learning across temporally correlated observations. The design reflects the assumption that temporal dimensions in affordance data are not semantically uniform and that discriminative information may be unevenly distributed across time. Experimental results on affordance datasets show that EEG-tGAT achieves improved classification performance compared to GATv2. The observed gains helps to conclude that explicitly encoding temporal importance and enforcing temporal robustness introduce inductive biases that are much better aligned with the structure of affordance-driven interaction data. These findings show us that modest architectural changes to graph attention models can help one obtain consistent benefits when temporal relationships play a nontrivial role in the task.
69e030705b704445ba50b545
Ami Chopra
claimed_verified
2026-04-16T08:02:07.357Z
66ce2f3a84b4e147ef3b76e5
https://cdn-avatars.huggingface.co/v1/production/uploads/66ce2f3a84b4e147ef3b76e5/gg_Bn4qoTn70f7ge7QzAh.jpeg
Ami Chopra
amichopra
user
amichopra
69e030705b704445ba50b546
Supriya Bordoloi
69e030705b704445ba50b547
Shyamanta M. Hazarika
A Temporally Augmented Graph Attention Network for Affordance Classification
Graph attention networks (GATs) provide one of the best frameworks for learning node representations in relational data; but, existing variants such as Graph Attention Network (GAT) mainly operate on static graphs and rely on implicit temporal aggregation when applied to sequential data. In this paper, we introduce Electroencephalography-temporal Graph Attention Network (EEG-tGAT), a temporally augmented formulation of GATv2 that is tailored for affordance classification from interaction sequences. The proposed model incorporates temporal attention to modulate the contribution of different time segments and temporal dropout to regularize learning across temporally correlated observations. The design reflects the assumption that temporal dimensions in affordance data are not semantically uniform and that discriminative information may be unevenly distributed across time. Experimental results on affordance datasets show that EEG-tGAT achieves improved classification performance compared to GATv2. The observed gains helps to conclude that explicitly encoding temporal importance and enforcing temporal robustness introduce inductive biases that are much better aligned with the structure of affordance-driven interaction data. These findings show us that modest architectural changes to graph attention models can help one obtain consistent benefits when temporal relationships play a nontrivial role in the task.
EEG-tGAT enhances Graph Attention Networks by incorporating temporal attention and dropout mechanisms to improve affordance classification from interaction sequences.
Graph Attention Networks GATv2 temporal attention temporal dropout affordance classification interaction sequences electroencephalography inductive biases