1 code implementation • NeurIPS 2021 • Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan
Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requirements for training, e. g., for calculating gradients via backprop over many data epochs.
1 code implementation • ICLR Workshop GTRL 2021 • Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg, Aram Galstyan
We consider two popular Graph Representation Learning (GRL) methods: message passing for node classification and network embedding for link prediction.