EmbeddingVis：一种比较网络嵌入检测的可视化分析方法
EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection
摘要：
通过嵌入模型构造网络中节点的潜在向量表示在许多图分析应用中显示了其实用性，例如节点分类、聚类和链接预测。然而，尽管学习嵌入模型的效率和准确性很高，但人们对嵌入向量中保留了原始网络的哪些信息几乎一无所知。低维向量表示的抽象性、构造过程的随机性以及非透明的超参数都模糊了对网络嵌入结果的理解。已经引入了可视化技术来促进嵌入向量检查，通常通过将嵌入空间投影到二维显示器。虽然现有的可视化方法允许简单的嵌入空间的结构检查，他们不能支持嵌入向量的深入探索。在本文中，我们设计了一个探索性的视觉分析系统，支持嵌入向量在集群，实例和结构层面的比较视觉解释。更具体地说，它有助于比较不同嵌入模型中节点度量的保存情况和保存方式，以及研究节点度量与所选嵌入向量之间的关系。几个案例研究证实了我们的系统的有效性。专家的反馈表明，我们的方法确实有助于他们更好地理解网络嵌入模型。
英文摘要：
Constructing latent vector representation for nodes in a network through embedding models has shown its practicality in many graph analysis applications, such as node classification, clustering, and link prediction. However, despite the high efficiency and accuracy of learning an embedding model, people have little clue of what information about the original network is preserved in the embedding vectors. The abstractness of low-dimensional vector representation, stochastic nature of the construction process, and non-transparent hyper-parameters all obscure understanding of network embedding results. Visualization techniques have been introduced to facilitate embedding vector inspection, usually by projecting the embedding space to a two-dimensional display. Although the existing visualization methods allow simple examination of the structure of embedding space, they cannot support in-depth exploration of the embedding vectors. In this paper, we design an exploratory visual analytics system that supports the comparative visual interpretation of embedding vectors at the cluster, instance, and structural levels. To be more specific, it facilitates comparison of what and how node metrics are preserved across different embedding models and investigation of relationships between node metrics and selected embedding vectors. Several case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps them better embrace the understanding of network embedding models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
