Abstract This study introduces BloomTree, an interactive Sunburst system for visualizing massive hierarchical datasets, such as the Tree of Life. Traditional static color schemes fail to maintain perceptual distinguishability when applied to millions of nodes, and managing the full tree in memory is computationally costly. BloomTree addresses these challenges through a combined strategy, centered on dynamic, view‐dependent, topology‐aware color allocation. This technique recomputes color assignments for the visible subtree after each navigation step. This ensures closely related regions appear with coherently related hues and adjacent sectors maintain sufficient contrast. Furthermore, to preserve the user's mental map during navigation, zooming operations are smoothly animated by simultaneously interpolating sector geometry and color assignments. For computational scalability, BloomTree uses selective subtree streaming and metanode aggregation to handle massive data, storing the full tree on a backend server and streaming only a truncated visible region. We evaluate BloomTree at the system and user levels. System analysis shows that selective streaming and metanode aggregation significantly improve computational scalability, reducing communication volume to about 1/70 and rendering cost to about 1/2,000. A controlled user study confirms that dynamic coloring yields faster responses and higher accuracy for several perceptual and structural tasks, highlighting the effectiveness of the system for exploring multi‐million‐node trees.

Visual Analytics (VA) supports iterative exploration and insight discovery, but current frameworks lack sufficient provenance management to capture the sequence of analytical actions and reasoning. This limitation hinders the reconstruction of analytical processes and the generation of coherent narratives. We propose the VASTorytelling Framework, which extends existing VA framework by incorporating mechanisms for recording user interactions, annotating visualizations, and utilizing extended data—including logs and external knowledge—for narrative construction. The framework supports metacognitive reflection and integrates large language models to assist narrative generation and reduce cognitive load. This approach enhances traceability, explainability, and communication in long-term VA activities.

Data analytics tools that combine automated text generation and visualization techniques suffer from scalability problems. The amount of the generated text explodes with the increase of items and attributes. This study addresses this problem for table data, whose attributes and data items are hierarchically organized. In our approach, the user's point of view is modeled by the dual focalization axis, which consists of the attribute-based and the data-item-based focal points. The user can refine the two-dimensional focal points to obtain the chart and the text that explain the data facts found in a more focused portion of the dataset. The proposal's efficacy was assessed through a quantitative and qualitative evaluation using a prototype visual analytics tool that employs the idea.

The paper tackles the problems of the “giant hairballs”, the dense and tangled structures often resulting from visualization of large social graphs. Proposed is a high-dimensional rotation technique called AGI3D, combined with an ability to filter elements based on social centrality values. AGI3D is targeted for a high-dimensional embedding of a social graph and its projection onto 3D space. It allows the user to rotate the social graph layout in the high-dimensional space by mouse dragging of a vertex. Its high-dimensional rotation effects give the user an illusion that he/she is destructively reshaping the social graph layout but in reality, it assists the user to find a preferred positioning and direction in the high-dimensional space to look at the internal structure of the social graph layout, keeping it unmodified. A prototype implementation of the proposal called Social Viewpoint Finder is tested with about 70 social graphs and this paper reports four of the analysis results.

Modularity is a widely used measure for evaluating community structure in networks. The definition of modularity involves a comparison between the observed network and a null model, which serves as a reference. To make the comparison significant, this null model should characterize some features of the observed network. However, the previously used null models are not good representations of real-world networks. A common feature of many real-world networks is similarity attraction, i.e., nodes that are similar have a higher chance of getting connected. We propose a new null model that captures this feature. Based on our null model, we create a unified measure Dist-Modularity, which incorporates the famous Newman-Girvan modularity as a special case. We use three examples to demonstrate that Dist-Modularity is useful in detecting 1) the multi-resolution communities and 2) the geographically dispersed communities.

Community analysis algorithm proposed by Clauset, Newman, and Moore (CNM algorithm) finds community structure in social networks. Unfortunately, CNM algorithm does not scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. We show that this inefficiency is caused from merging communities in unbalanced manner and that a simple heuristics that attempts to merge community structures in a balanced manner can dramatically improve community structure analysis. The proposed techniques are tested using data sets obtained from existing social networking service that hosts 5.5 million users. We have tested three three variations of the heuristics. The fastest method processes a SNS friendship network with 1 million users in 5 minutes (70 times faster than CNM) and another friendship network with 4 million users in 35 minutes, respectively. Another one processes a network with 500,000 nodes in 50 minutes (7 times faster than CNM), finds community structures that has improved modularity, and scales to a network with 5.5 million.

Failure in visual communication between the author and the colorblind reader is caused when color effects that the author expects for the reader to experience are not observed by the reader. The proposed framework allows the author to annotate his/her intended color effects to the colored document. They are used to generate a repainted document that let the colorblind enjoy similar color effects that normal color vision person does for the original document. The annotations are formulated as a set of mathematical constraints that can describe several commonly used color effects. Constraints are defined over the normal vision color space. Then they are projected onto the restricted color space that corresponds to the one that the colorblind perceives. Finally, the projected constraints are resolved for the search of best repainting of the document that most successfully presents to the colorblind person the color effects experienced by the normal vision person on the original document. Effectiveness of the proposal is shown by colorblind simulation.