Scipediacontent: Scipediacontent moved page Draft Content 979471146 to Zhao et al 2018a

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Scipediacontent moved page Draft Content 979471146 to Zhao et al 2018a

Scipediacontent: Created page with " == Abstract == Local rare event forecasting and synthesis on networks are highly useful for emergence management. For example, synthesizing traffic congestion and disease di..."

2021-02-01T15:11:39Z

Created page with " == Abstract == Local rare event forecasting and synthesis on networks are highly useful for emergence management. For example, synthesizing traffic congestion and disease di..."

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== 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.

== Original document ==

The different versions of the original document can be found in:

* [https://dl.acm.org/doi/pdf/10.1145/3282866.3282872?download=true https://dl.acm.org/doi/pdf/10.1145/3282866.3282872?download=true]

* [https://dblp.uni-trier.de/db/conf/gis/lens2018.html#GaoG018 https://dblp.uni-trier.de/db/conf/gis/lens2018.html#GaoG018],
: [https://academic.microsoft.com/#/detail/2912203876 https://academic.microsoft.com/#/detail/2912203876]

* [https://dl.acm.org/doi/pdf/10.1145/3282866.3282872 https://dl.acm.org/doi/pdf/10.1145/3282866.3282872],
: [http://dx.doi.org/10.1145/3282866.3282872 http://dx.doi.org/10.1145/3282866.3282872] under the license http://www.acm.org/publications/policies/copyright_policy#Background

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Zhao et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 979471146 to Zhao et al 2018a

Scipediacontent: Created page with " == Abstract == Local rare event forecasting and synthesis on networks are highly useful for emergence management. For example, synthesizing traffic congestion and disease di..."