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STGGAN: Spatial-temporal Graph Generation

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SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 608 - 609

https://doi.org/10.1145/3347146.3363462

Published: 05 November 2019 Publication History

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Abstract

This work addresses research on spatial-temporal graph generation and introduces a new model called "Spatial-temporal Graph Generative Adversarial Network" (STGGAN), which uses a newly developed variable-length Long Short Term Memory network via spatial-temporal random walks.

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Cited By

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Bhati BEckhoff D(2025)Synthetic Mobility TracesEncyclopedia of Cryptography, Security and Privacy10.1007/978-3-030-71522-9_1734(2570-2574)Online publication date: 8-Jan-2025
https://doi.org/10.1007/978-3-030-71522-9_1734
Ribeiro IComarela GRocha AMota V(2024)Towards a Framework to Evaluate Generative Time Series Models for Mobility Data FeaturesJournal of Internet Services and Applications10.5753/jisa.2024.388715:1(258-272)Online publication date: 11-Aug-2024
https://doi.org/10.5753/jisa.2024.3887
Li TLi RYe SZhang ZYin ZLi SPan Z(2024)CRTGAN: Controllable Road Network Graphs Generation via Transformer based GAN2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650466(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650466
Show More Cited By

Index Terms

STGGAN: Spatial-temporal Graph Generation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
        Deep belief networks
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Information & Contributors

Information

Published In

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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Extended-abstract
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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

Acceptance Rates

SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

View all

Bhati BEckhoff D(2025)Synthetic Mobility TracesEncyclopedia of Cryptography, Security and Privacy10.1007/978-3-030-71522-9_1734(2570-2574)Online publication date: 8-Jan-2025
https://doi.org/10.1007/978-3-030-71522-9_1734
Ribeiro IComarela GRocha AMota V(2024)Towards a Framework to Evaluate Generative Time Series Models for Mobility Data FeaturesJournal of Internet Services and Applications10.5753/jisa.2024.388715:1(258-272)Online publication date: 11-Aug-2024
https://doi.org/10.5753/jisa.2024.3887
Li TLi RYe SZhang ZYin ZLi SPan Z(2024)CRTGAN: Controllable Road Network Graphs Generation via Transformer based GAN2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650466(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650466
Yang YChen CLi T(2024)Automated construction site layout design system for prefabricated buildings using transformer based conditional GANAdvanced Engineering Informatics10.1016/j.aei.2024.10288562:PDOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.aei.2024.102885
Rassil AChougrad HZouaki H(2023)Deep multi-agent fusion Q-Network for graph generationKnowledge-Based Systems10.1016/j.knosys.2023.110509269(110509)Online publication date: Jun-2023
https://doi.org/10.1016/j.knosys.2023.110509
Guo XZhao L(2022)A Systematic Survey on Deep Generative Models for Graph GenerationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.3214832(1-20)Online publication date: 2022
https://doi.org/10.1109/TPAMI.2022.3214832
Faez FOmmi YBaghshah MRabiee H(2021)Deep Graph Generators: A SurveyIEEE Access10.1109/ACCESS.2021.30984179(106675-106702)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3098417
Bhati BEckhoff D(2021)Synthetic Mobility TracesEncyclopedia of Cryptography, Security and Privacy10.1007/978-3-642-27739-9_1734-1(1-5)Online publication date: 7-Aug-2021
https://doi.org/10.1007/978-3-642-27739-9_1734-1

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