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pro vyhledávání: '"Zhou, Haicang"'
Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemp
Externí odkaz:
http://arxiv.org/abs/2406.04038
Graph attention networks (GATs) are powerful tools for analyzing graph data from various real-world scenarios. To learn representations for downstream tasks, GATs generally attend to all neighbors of the central node when aggregating the features. In
Externí odkaz:
http://arxiv.org/abs/2210.07715
Large-scale network embedding is to learn a latent representation for each node in an unsupervised manner, which captures inherent properties and structural information of the underlying graph. In this field, many popular approaches are influenced by
Externí odkaz:
http://arxiv.org/abs/2006.16499
Akademický článek
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