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Spatio-Temporal Deep Graph Infomax

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Opolka, Felix L 
Solomon, Aaron 
Cangea, Cătălina 
Veličković, Petar 
Liò, Pietro 

Abstract

Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time. To address these issues, we introduce Spatio-Temporal Deep Graph Infomax (STDGI)---a fully unsupervised node representation learning approach based on mutual information maximization that exploits both the temporal and spatial dynamics of the graph. Our model tackles the challenging task of node-level regression by training embeddings to maximize the mutual information between patches of the graph, at any given time step, and between features of the central nodes of patches, in the future. We demonstrate through experiments and qualitative studies that the learned representations can successfully encode relevant information about the input graph and improve the predictive performance of spatio-temporal auto-regressive forecasting models.

Description

Keywords

cs.LG, cs.LG, stat.ML

Journal Title

CoRR

Conference Name

Seventh International Conference on Learning Representations (ICLR 2019)

Journal ISSN

Volume Title

Publisher