Abstract: Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity and can only handle graphs with at most thousands of nodes. To this ...
F. Gama, A. G. Marques, G. Leus, and A. Ribeiro, "Convolutional Neural Network Architectures for Signals Supported on Graphs," IEEE Trans. Signal Process., vol. 67 ...
Abstract: Graph representation is an important part of graph clustering. Recently, contrastive learning, which maximizes the mutual information between augmented graph views that share the same ...
Inferring road graphs from satellite imagery is a challenging computer vision task. Prior solutions fall into two categories: (1) pixel-wise segmentation-based approaches, which predict whether each ...
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