Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang. In addition, GAOs belong to the family of soft attention, instead of hard attention, which has been shown to yield better performance.
Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes. Data Representation and Learning with Graph Diffusion-Embedding Networks.
Graph Learning-Convolutional Networks.
paper. CVPR 2019. paper. Graph Representation Learning via Hard and Channel-Wise Attention Networks.
NeurIPS 2019. paper.
The 36th International Conference on Machine Learning (ICML), 2083-2092, 2019 Hongyang Gao and Shuiwang Ji Graph Representation Learning via Hard and Channel-Wise Attention Networks The 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 741-749, 2019
hGAO uses the hard attention mechanism by attending to only important nodes. Graph Representation Learning via Hard and Channel-Wise Attention Networks. 2019 KDD. Graph Representation Learning via Hard and Channel-Wise Attention Networks Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning.
In this work, we propose novel hard graph attention operator~(hGAO) and channel-wise graph attention operator~(cGAO). Hongyang Gao, Shuiwang Ji. 3.1 Hard Graph Attention Operator Graph attention operator (GAO) consumes excessive computation resources, including computational cost and memory usage, when graphs have a large number of nodes, which is very common in real-world applications. Learning convolutional neural networks for graphs. Hongyang Gao, Shuiwang Ji. Convolution. CVPR 2019. paper. Boris Knyazev, Graham W. Taylor, Mohamed R. Amer.
However, graph attention operators (GAOs) consume excessive computational resources, preventing their applications on large graphs.
KDD 2019. paper. Understanding Attention and Generalization in Graph Neural Networks. Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes.
Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning. propose the deep graph attention networks for network embedding learning.
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