The hold-out test set was created by randomly sampling 10% of the positive and negative links. The remaining links were split into 5 folds where one such fold made up the validation set, and the rest the training set. The positive links in the training set were included in both directions because the SEAL model considers the directionality of the edges. This resulted in twice as many positive links as negative links in the training set. The number of links in each set are displayed in Table . Hyperparameter optimization was performed using Optuna . Here, 50% of the training edges were included in the training during the hyperparameter optimization and 50% of the validation set were used to evaluate of the parameters as the hyperparameter process as to speed up this time-consuming process. Optuna was initialized with the Tree-structured Parzen Estimator (TPE) sampling option, with 40 random startup trials, in total 100 trials were completed. The hyperparameter search-space and the result of the optimization is reported in the supplementary materials. For further explanations of the hyperparameters we refer to the original publication .