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.

中文翻译：

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网络表示学习方法的实证评估

网络表示学习方法将网络节点映射到嵌入空间中的向量，该向量可以保留特定属性并启用传统的下游预测任务。然后，通常通过这些下游任务的结果来展示学习到的表示的质量。然而，常用的基准测试任务（例如链路预测或网络重建）呈现出复杂的评估流程和丰富的设计选择。再加上缺乏标准化的评估设置，可能会掩盖该领域的真正进展。在本文中，我们旨在调查各种此类设计选择对性能的影响，并进行广泛且一致的评估，以阐明网络表示学习的最新技术。