Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs
Keywords: Data, Dataset, PDE, Graph, Spatio-Temporal, Epidemiology, Benchmarking
TL;DR: We introduce three synthetic temporal graph datasets, along with a method and code for their and similar datasets creation, and demonstrate their general use throughout benchmarking and Transfer learning on real-world tasks.
Abstract: In this work, we describe the creation and use of synthetic datasets based on various partial differential equations to support spatio-temporal graph modeling in machine learning for different applications. More precisely, we showcase three equations to model different types of disasters and hazards in the fields of epidemiology, atmospheric particles, and tsunami waves. Further, we show how such created datasets can be used by benchmarking several machine learning models on the epidemiological dataset and, additionally, by showing how pre-training on such synthetic datasets can improve model performance on real-world epidemiological data. The presented methods enable others to create datasets and benchmarks customized to individual requirements. The source code for our methodology and the three created datasets can be found on https://github.com/github-usr-ano/Temporal_Graph_Data_PDEs .
Primary Area: datasets and benchmarks
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Submission Number: 7419
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