INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging-Reference-Cited by-同舟云学术

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2023
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Author:

Zheng Chuanpan¹^ORCID,Fan Xiaoliang¹^ORCID,Wang Cheng¹^ORCID,Qi Jianzhong²^ORCID,Chen Chaochao³^ORCID,Chen Longbiao¹^ORCID

Affiliation:

1. School of Informatics, Xiamen University, China

2. University of Melbourne, Australia

3. Zhejiang University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543507.3583525

Reference54 articles.

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2. Mohammad Taha Bahadori Rose Yu and Yan Liu. 2014. Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning. In NeurIPS. 3491–3499. Mohammad Taha Bahadori Rose Yu and Yan Liu. 2014. Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning. In NeurIPS. 3491–3499.

3. Buru Chang Yonggyu Park Donghyeon Park Seongsoon Kim and Jaewoo Kang. 2018. Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation. In IJCAI. 3301–3307. Buru Chang Yonggyu Park Donghyeon Park Seongsoon Kim and Jaewoo Kang. 2018. Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation. In IJCAI. 3301–3307.

4. SDCAE: Stack Denoising Convolutional Autoencoder Model for Accident Risk Prediction Via Traffic Big Data

5. Time-Dependent Visiting Trip Planning with Crowd Density Prediction Based on Internet of Things Localization

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