Negative sampling normalizes the embeddings of relations which improves the robustness of the model

Negative sampling is needed for loss function calculation, for example Margin Ranking Loss 

Negative sampling restores the missed negative edges in the graph, thus, improves the accuracy of the model

Negative sampling is mostly used as data augmentation for better training convergence