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Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction

Research article (Knowledge-Based Systems, 2023) · cited 40× · AI/ML
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Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction

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Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction is a scholarly article[1].

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  • Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction. Retrieved May 24, 2026, from https://4ort.xyz/entity/domain-adversarial-graph-neural-network-with-cross-city-graph-structure-learning-for-traffic-prediction
MLA “Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/domain-adversarial-graph-neural-network-with-cross-city-graph-structure-learning-for-traffic-prediction.
BibTeX @misc{4ortxyz_domain-adversarial-graph-neural-network-with-cross-city-graph-structure-learning-for-traffic-prediction_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction}}, year = {2026}, url = {https://4ort.xyz/entity/domain-adversarial-graph-neural-network-with-cross-city-graph-structure-learning-for-traffic-prediction}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction — https://4ort.xyz/entity/domain-adversarial-graph-neural-network-with-cross-city-graph-structure-learning-for-traffic-prediction (retrieved 2026-05-24)

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