Adaptive graph neural network training objective for link predictionPOŠTUVAN, TIM (Avtor) Šubelj, Lovro (Mentor) machine learningmeta-learninglink predictiongraph neural networkstraining objectiveOne of the most important factors which contributes to success of a machine learning model is good training objective. Training objective crucially influences model’s performance and generalization capabilities. We focus on graph neural network training objective for link prediction, because it is barely explored in literature. In this case, training objective includes, among others, training mode, negative sampling strategy, and various hyperparameters, such as edge message ratio. Commonly, these hyperparameters are fine-tuned by complete search, which is very time consuming and model dependent. To mitigate these limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically adjusts edge message ratio during training. It is model agnostic and highly scalable with fully customizable computational budget. AdaGrid can also boosts performance of the models up to 2.3%, while can be nine times more efficient than complete search.20212021-08-17 13:10:00Diplomsko delo/naloga128919VisID: 32301COBISS_ID: 75109379sl