Before revision: Network representation learning has become an active research area in recent years with many new methods showcasing their performance on downstream prediction tasks such as Link Prediction. Despite the efforts of the community to ensure reproducibility of research by providing method implementations, important issues remain. The complexity of the evaluation pipelines and  abundance of design choices have led to difficulties in quantifying the progress in the field and identifying the state-of-the-art .
After revision: Network embedding methods map a network's nodes to vectors in an embedding space, in such a way that these representations are useful for estimating some notion of similarity or proximity between pairs of nodes in the network. The quality of these node representations is then showcased through results of downstream prediction tasks. Commonly used benchmark tasks such as link prediction, however, present complex evaluation pipelines and abundance of design choices have led to difficulties in quantifying the progress in the field and identifying the state-of-the-art .