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 . In this work, we analyse 17 network embedding methods on 7 real-world datasets and find , using a consistent evaluation pipeline,  only thin progress over the recent years. Also, many embedding methodsare outperformed by simple heuristics. Finally, we discuss how standardized evaluation tools can repair this situation and boost  progress in this field.
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 an abundance of design choices . This, together with a lack of standardized evaluation setups can obscure the real progress in the field . In this paper, we aim to shed light on the state-of-the-art of network embedding methods for link prediction and show , using a consistent evaluation pipeline, that only thin progress has been made over the last years. The newly conducted benchmark that we present here, including 17 embedding methods, also shows that many approaches are outperformed even by simple heuristics. Finally, we argue that standardized evaluation tools can repair this situation and boost future progress in this field.
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