To train SEAL, a dataset consisting of a positive and negative class was used. The positive class consists of the positive links (observed reactions) in the CRKG while the negative class was constructed from the negative links (unobserved reactions). However, as the number of negative links scales exponentially with the number of nodes in the graph, using all negative links as the negative class would both make a highly unbalanced dataset and be unfeasible to train on. Instead, one negative link was sampled for each positive link. For this task, we use two different negative sampling strategies: Random sampling and Node degree-preserving sampling (NDP), where we sampled equal number of negative links with each strategy. Random sampling means that the source and target nodes were sampled with replacement from the set of nodes in the graph. While NDP sampling results in a preservation of the nodes' occurrence count in the positive link set by sampling the source and target nodes without replacement, i.e., all nodes have the exact same degree distribution when comparing separately positive and negative links.