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Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks.
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Simple unsupervised graph representation learning
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Spatial transition learning on road networks with deep probabilistic models
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Road network representation learning with vehicle trajectories
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Road network representation learning: A dual graph based approach
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Structcomp: Substituting propagation with structural compression in training graph contrastive learning
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