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
4 Pith papers cite this work. Polarity classification is still indexing.
4
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.
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 Star Net for Generalized Multi-Task Learning
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.
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
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.
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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.