We present the first empirical evaluation of techniques for encoding distributions of quantitative edge values within adjacency matrices. In many real-world networks, edges represent not a single value but a set of measurements. While adjacency matrices preserve structural clarity, their compact cells limit the simultaneous display of multiple values. To address this, we explore edge encodings that represent distributions by two values: a measure of central tendency (mean, median, mode) and a measure of dispersion (standard deviation, variance, IQR). We select four possible encodings for evaluation that prior work has suggested are suitable for the limited space available in matrices: a bivariate color palette, embedded bar charts, and two overlaid-mark designs mapping the primary attribute to color and the secondary attribute to area or angle. In a preregistered crowdsourced study with 156 participants, we assessed performance of these encodings across eight analytical tasks and collected readability and aesthetic ratings. Results reveal clear performance regimes: area-based overlaid marks and bar charts achieved the highest overall performance; angle-based marks show moderate but less stable performance,and bivariate color consistently underperforms these alternatives. These findings clarify how visual channels behave under strict constraints and delineate the strengths and limitations of key design choices for multivariate edge visualization.

翻译：我们提出了首个针对邻接矩阵内定量边值分布编码技术的经验评估。在诸多真实网络场景中，边所代表的并非单一数值，而是一系列测量值。尽管邻接矩阵能够保持结构的清晰性，但其紧凑的单元格限制了多个数值的同时显示。为解决这一问题，我们探索了通过两个值表征分布的边编码方案：集中趋势度量（均值、中位数、众数）与离散程度度量（标准差、方差、四分位距）。基于先前研究对矩阵有限空间适用性的建议，我们选取四种备选编码进行评估：双变量颜色调色板、嵌入式条形图，以及两种叠加标记设计（将主属性映射至颜色，次属性分别映射至面积或角度）。通过一项包含156名参与者的预注册众包研究，我们评估了这些编码在八类分析任务中的表现，并收集了可读性与美观度评分。结果揭示了明确的性能差异：基于面积的叠加标记与条形图取得了最高的整体性能；基于角度的标记表现中等但稳定性较差；而双变量颜色在各类场景中始终逊于其余方案。这些发现阐明了视觉通道在严格约束条件下的行为规律，并揭示了多变量边可视化中关键设计权衡的优势与局限。