Graph Time-series Modeling in Deep Learning: A Survey

Author:

Chen Hongjie1,Eldardiry Hoda1

Affiliation:

1. Virginia Tech, USA

Abstract

Time-series and graphs have been extensively studied for their ubiquitous existence in numerous domains. Both topics have been separately explored in the field of deep learning. For time-series modeling, recurrent neural networks or convolutional neural networks model the relations between values across time steps, while for graph modeling, graph neural networks model the inter-relations between nodes. Recent research in deep learning requires simultaneous modeling for time-series and graphs when both representations are present. For example, both types of modeling are necessary for time-series classification, regression, and anomaly detection in graphs. This paper aims to provide a comprehensive summary of these models, which we call graph time-series models. To the best of our knowledge, this is the first survey paper that provides a picture of related models from the perspective of deep graph time-series modeling to address a range of time-series tasks, including regression, classification, and anomaly detection. Graph time-series models are split into two categories, a) graph recurrent/convolutional neural networks and b) graph attention neural networks. Under each category, we further categorize models based on their properties. Additionally, we compare representative models and discuss how distinctive model characteristics are utilized with respect to various model components and data challenges. Pointers to commonly used datasets and code are included to facilitate access for further research. In the end, we discuss potential directions for future research.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference102 articles.

1. Uri Alon and Eran Yahav . 2020 . On the Bottleneck of Graph Neural Networks and its Practical Implications . In International Conference on Learning Representations. Uri Alon and Eran Yahav. 2020. On the Bottleneck of Graph Neural Networks and its Practical Implications. In International Conference on Learning Representations.

2. Anthony Bagnall , Hoang Anh Dau , Jason Lines , Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh. 2018 . The UEA multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075(2018). Anthony Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh. 2018. The UEA multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075(2018).

3. D. Bahdanau K. Cho and Y. Bengio. 2015. Neural machine translation by jointly learning to align and translate. ICLR 1409(2015). D. Bahdanau K. Cho and Y. Bengio. 2015. Neural machine translation by jointly learning to align and translate. ICLR 1409(2015).

4. A Survey on Embedding Dynamic Graphs

5. Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

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