发表机构
Arizona State University; University of California, Berkeley; ICSI（亚利桑那州立大学; 加州大学伯克利分校; 国际计算机科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究分子图神经网络潜在空间分析问题，提出LatentFlow可视化分析系统，通过聚类嵌入、跟踪跨层及模型状态变化、关联代表性分子等方法，助力科学家理解潜在空间演变、识别分子模式及解释模型行为。
AI 中文摘要
化学家和材料科学家越来越多地使用机器学习模型（如图神经网络，GNNs）来预测分子性质及其反应结果。除了预测性能，理解这些模型如何在其潜在空间（即分子的嵌入）中内部组织化学信息至关重要。分析潜在空间有助于诊断模型行为并评估所学嵌入是否以反映有意义化学关系的方式组织。现有方法对跨层和不同模型状态（如训练轮次、模型配置和输入数据）分析潜在空间支持有限。我们提出LatentFlow，一个与领域专家合作开发的用于分析分子GNNs潜在空间的可视化分析系统。LatentFlow将嵌入聚类，并通过使用修改后的桑基图跟踪这些聚类如何跨层和模型状态变化来支持潜在空间探索。为支持解释，LatentFlow将这些聚类与代表性分子及其共享子结构联系起来，并允许科学家引入自己的领域知识并与潜在空间中发现的模式进行比较。我们通过两个案例研究评估LatentFlow。结果表明，LatentFlow有助于科学家理解潜在空间如何演变，识别有意义的分子模式并更好地解释模型行为。
英文摘要
Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and the outcomes of their reactions. Beyond predictive performance, understanding how these models organize chemical information internally in their latent spaces, i.e., the embeddings of the molecules, is critical. Analyzing latent spaces helps diagnose model behavior and assess whether the learned embeddings are organized in ways that reflect meaningful chemical relationships. Unfortunately, existing methods provide limited support for analyzing latent spaces across layers and across different model states (e.g., training epochs, model configurations, and input data), making it difficult to understand how these latent spaces evolve throughout a model or relate to chemical concepts. We present LatentFlow, a visual analytics system developed in collaboration with a domain expert for analyzing latent spaces in molecular GNNs. LatentFlow groups embeddings into clusters and supports exploration of latent spaces by tracking how these clusters change across layers and model states using a modified Sankey diagram. To support interpretation, LatentFlow links these clusters to representative molecules and their shared substructures, and it allows scientists to introduce their own domain knowledge and compare it with the patterns found in the latent spaces. We evaluate LatentFlow through two case studies. The results show that LatentFlow helps scientists understand how latent spaces evolve, identify meaningful molecular patterns, and better interpret model behavior.
