Harsh188/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.

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.