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Local Event Forecasting and Synthesis Using Unpaired Deep Graph Translations

Authors:

Liang ZhaoAuthors Info & Claims

LENS'18: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News

Article No.: 5, Pages 1 - 8

https://doi.org/10.1145/3282866.3282872

Published: 06 November 2018 Publication History

Abstract

Local rare event forecasting and synthesis on networks are highly useful for emergence management. For example, synthesizing traffic congestion and disease diffusion over the road network and disease-contact network respectively of specific geo-locations is highly important for transportation planning and disease outbreaks intervention. This task requires to learn how the events of congestion or disease "translate" the graph patterns from source mode (e.g., without event) to target mode (e.g., with event) based on historical data for some locations. Then it needs to apply such "translation" upon a source-mode graph pattern in a new location's network, in order to estimate and foresee what it will look like in target-mode in this location.

Such task is called graph translation, which is an analogy and generalization to image and text translation. Similar to the situations in image and text translation, paired training data, which consists of pairs of source-mode graph and its corresponding target-mode, will usually not be available. In this work, we propose an approach for learn the translation of graphs from source-mode to target-mode such that the generated target-mode is indistinguishable from the distribution of the real target-mode using an adversarial loss. Because there is no paired training data, we also learn an inverse translation from target-mode to source-mode and couple these two translation mappings through cycle consistency loss. Extensive experiments on both synthetic and real-world application data demonstrate that the proposed approaches is capable of generating graphs close to real target graphs. Case studies on the synthesized networks have also been illustrated and analyzed to show the reasonableness of the generated target-mode graphs.

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Cited By

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
Luo GLi CCui HSun LHe LYang C(2022)Multi-View Brain Network Analysis with Cross-View Missing Network Generation2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995283(108-115)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9995283
Faustino A(2021)Graphs based on IR as Representation of CodeProceedings of the 25th Brazilian Symposium on Programming Languages10.1145/3475061.3475063(75-82)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3475061.3475063
Show More Cited By

Index Terms

Local Event Forecasting and Synthesis Using Unpaired Deep Graph Translations
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Conferences

LENS'18: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News

November 2018

49 pages

ISBN:9781450360357

DOI:10.1145/3282866

Editors:
Amr Magdy
University of California, Riverside, CA, USA
,
Xun Zhou
University of Iowa, IA, USA
,
Liang Zhao
George Mason University, VA, USA
,
Yan Huang
University of North Texas, TX, USA

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 November 2018

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SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6, 2018

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Cited By

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
Luo GLi CCui HSun LHe LYang C(2022)Multi-View Brain Network Analysis with Cross-View Missing Network Generation2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995283(108-115)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9995283
Faustino A(2021)Graphs based on IR as Representation of CodeProceedings of the 25th Brazilian Symposium on Programming Languages10.1145/3475061.3475063(75-82)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3475061.3475063
Guo XZhao LHomayoun HDinakarrao S(2021)Deep graph transformation for attributed, directed, and signed networksKnowledge and Information Systems10.1007/s10115-021-01553-9Online publication date: 3-Apr-2021
https://doi.org/10.1007/s10115-021-01553-9

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