We investigate tasks that can be accomplished with unlabeled graphs, which are graphs with nodes that do not have persistent or semantically meaningful labels attached. New visualization techniques to represent unlabeled graphs have been proposed, but more understanding of unlabeled graph tasks is required before these techniques can be adequately evaluated. Some network visualization tasks apply to both labeled and unlabeled graphs, but many do not translate between these contexts. We propose a data abstraction model that distinguishes the Unlabeled context from the increasingly semantically rich Labeled, Attributed, and Augmented contexts. We filter tasks collected and gleaned from the literature according to our data abstraction and analyze the surfaced tasks, leading to a taxonomy of abstract tasks for unlabeled graphs. Our task taxonomy is organized according to the Target data under consideration, the Action intended by the user, and the Scope of the data at play. We show the descriptive power of this task abstraction by connecting to concrete examples from previous frameworks, and connecting these abstractions to real-world problems. To showcase the evaluative power of the taxonomy, we perform a preliminary assessment across 6 different network visualization idioms for each task. For each combination of task and visual encoding, we consider the effort required from viewers, the likelihood of task success, and how both factors vary between small-scale and large-scale graphs. Supplemental materials are available at osf.io/e23mr.

翻译：本研究探讨了在未标注图（即节点不具备持久性或语义性标签的图）上可完成的任务。虽然已有新提出的可视化技术用于呈现未标注图，但在充分评估这些技术之前，需深入理解未标注图的任务特征。部分网络可视化任务同时适用于标注图与未标注图，但多数任务无法在两类场景间直接迁移。我们提出一种数据抽象模型，将未标注场景与语义性递增的标注场景、属性场景及增强场景区分开来。依据该数据抽象模型，我们对从文献中收集并筛选的任务进行过滤与分析，进而构建未标注图抽象任务分类体系。该分类体系从目标数据、用户意图及数据作用域三个维度组织任务。通过关联既有框架中的具体实例并连接至实际问题，我们展示了该任务抽象的描述能力。为验证分类体系的评估能力，我们针对每项任务在6种不同网络可视化范式下进行初步评估。对于每种任务与视觉编码的组合，我们考量了观测者的认知负荷、任务成功概率，以及两种因素在小规模图与大规模图之间的差异。补充材料详见osf.io/e23mr。