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Iteratively Tuning Graph Neural Networks

Luis Bermudez takes us through the process of tuning one flavor of neural network:

We made our own implementations of OGB leaderboard entries for two popular GNN frameworks: GraphSAGE and a Relational Graph Convolutional Network (RGCN). We then designed and executed an iterative experimentation approach for hyperparameter tuning where we seek a quality model that takes minimal time to train. We define quality by running an unconstrained performance tuning loop, and use the results to set thresholds in a constrained tuning loop that optimizes for training efficiency.

Read on to see how they did it.