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One of the most important factors that contribute to the success of a machinelearning model is a good training objective. Training objective cruciallyinfluences the model's performance and generalization capabilities. This paperspecifically focuses on graph neural network training objective for linkprediction, which has not been explored in the existing literature. Here, thetraining objective includes, among others, a negative sampling strategy, andvarious hyperparameters, such as edge message ratio which controls how trainingedges are used. Commonly, these hyperparameters are fine-tuned by complete gridsearch, which is very time-consuming and model-dependent. To mitigate theselimitations, we propose Adaptive Grid Search (AdaGrid), which dynamicallyadjusts the edge message ratio during training. It is model agnostic and highlyscalable with a fully customizable computational budget. Through extensiveexperiments, we show that AdaGrid can boost the performance of the models up to$1.9\%$ while being nine times more time-efficient than a complete search.Overall, AdaGrid represents an effective automated algorithm for designingmachine learning training objectives.

AdaGrid: Adaptive Grid Search for Link Prediction Training Objective