GraphStar is a new GNN that adds star nodes and relay attention to achieve non-local representations for node, graph, and link tasks, claiming 2-5% gains over prior SOTA on benchmarks.
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head's importance. We demonstrate the effectiveness of GaAN on the inductive node classification problem. Moreover, with GaAN as a building block, we construct the Graph Gated Recurrent Unit (GGRU) to address the traffic speed forecasting problem. Extensive experiments on three real-world datasets show that our GaAN framework achieves state-of-the-art results on both tasks.
representative citing papers
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
GMENet synthesizes missing MRI sequences with gated cross-attention and fuses dual-sequence features via confidence-aware mixture-of-experts for improved glioma diagnosis on incomplete multi-center data.
GLGAT uses global-local graph attention with pairwise encoding and event-based adjacency to capture spatio-temporal traffic correlations and reports competitive results on two real-world datasets.
citing papers explorer
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Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
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GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences
GMENet synthesizes missing MRI sequences with gated cross-attention and fuses dual-sequence features via confidence-aware mixture-of-experts for improved glioma diagnosis on incomplete multi-center data.
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A Global-Local Graph Attention Network for Traffic Forecasting
GLGAT uses global-local graph attention with pairwise encoding and event-based adjacency to capture spatio-temporal traffic correlations and reports competitive results on two real-world datasets.