An empirical evaluation of network representation learning methods

Network representation learning methods map network nodes to vectors in an embedding space that can preserve specific properties and enable traditional downstream prediction tasks. The quality of the representations learned is then generally showcased through results on these downstream tasks. Commonly used benchmark tasks such as link prediction or network reconstruction, 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 article, we aim at investigating the impact on the performance of a variety of such design choices and perform an extensive and consistent evaluation that can shed light on the state-of-the-art on network representation learning. Our evaluation reveals that only limited progress has been made in recent years, with embedding-based approaches struggling to outperform basic heuristics in many scenarios