  One of the most important factors that contribute to the success of a machine
learning model is a good training objective. Training objective crucially
influences the model's performance and generalization capabilities. This paper
specifically focuses on graph neural network training objective for link
prediction, which has not been explored in the existing literature. Here, the
training objective includes, among others, a negative sampling strategy, and
various hyperparameters, such as edge message ratio which controls how training
edges are used. Commonly, these hyperparameters are fine-tuned by complete grid
search, which is very time-consuming and model-dependent. To mitigate these
limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically
adjusts the edge message ratio during training. It is model agnostic and highly
scalable with a fully customizable computational budget. Through extensive
experiments, 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 designing
machine learning training objectives.
