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cs.CV2023
Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks
Yang Li, Yuichi Tanaka
We present an attention-based spatial graph convolution (AGC) for graph neural networks (GNNs). Existing AGCs focus on only using node-wise features and utilizing one type of atten…
cs.CV2021
Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks
Yang Li, Yuichi Tanaka
In this paper, we propose a spatial graph convolution (GC) to classify signals on a graph. Existing GC methods are limited to using the structural information in the feature space.…