GraphCoReg

Graphs are widely in use to model related instances of data attributed with properties providing rich spatial information. While a lot of classical graph-related problems have been solved with the advent of Graph Neural Networks (GNN), Spatio-Temporal data poses a new challenge. We propose GraphCoReg: a novel methodology to perform regression on spatio-temporal data, in a Semi-Supervised Learning (SSL) setting using co-training. Our co-training approach exploits two views of the dataset using two temporal Graph Neural Networks (GNNs) - an Attention-based GNN (A3TGCN) and a Long Short Term Memory GNN (GCLSTM). Additionally, methodologies to incrementally add the pseudo-targets to training data have been described. We finally compare the performance of the semi-supervised model with equivalent supervised models. This approach has been tested on the MetrLA dataset for traffic forecasting.