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We present BLIMMP-Explorer, an interactive visualization system for BLIMMP, a Bayesian tool we developed for inferring metabolic module presence from prokaryotic genomes. Probabilistic tools for metabolic module inference produce layered evidence that tabular outputs cannot clearly communicate. BLIMMP-Explorer renders each module as a gene-centric directed acyclic graph with split-circle nodes encoding two independent evidence values, step-level probability annotations, and flag-based indicators of annotation quality. An influence panel decomposes the probability shift contributed by associated enzymes across a global co-occurrence network. We demonstrate the system on Pseudomonas fluorescens SBW25 through two use cases: (i) recovering a pathway in an incomplete genome and (ii) identifying a false positive by reviewing annotation flags and competing enzyme annotations. The visualization enables reasoning that would otherwise require manual cross-referencing of multiple output files.

Exploring similar nodes in attributed networks represents a key challenge in data mining. While recent representation learning methods embed networks into low-dimensional vectors, they often implicitly assume a uniform and continuous feature space. This paper proposes a visual analytics approach using dimensionality reduction to help clarify the true topological structure of high-dimensional feature spaces formed by nodes' neighborhood attribute profiles. Analyzing inter-firm transaction networks indicates that structural roles can form complex, non-linear manifolds with density biases. Comparing this feature space with industry classifications suggested: (1) supply chain hierarchies transition continuously; (2) categories treated identically under general semantics can be clearly separated by actual transaction networks; and (3) a single industry label may fragment into multiple regions. These findings suggest potential limitations in assuming identical semantics imply similar structural roles and highlight the possible need for new similarity metrics aligned with manifold topology.

Paleoecological researchers rely on large community-curated databases such as Neotoma to locate, interpret, and compare taxa across geologic timescales. Existing query interfaces assume users already know what they are looking for and offer little support for exploring the hierarchical taxonomic structure itself. Neotoma's taxonomy compounds this problem: its 67,885 taxa span 52 taxagroups that vary from a handful of nodes to nearly twenty thousand, follow a non-Linnaean parent-child structure without fixed rank assignments, and carry historical naming ambiguities accumulated over a century of curation. We present a two-part formative design study focused on detailing our process of, and key takeaways from, developing the Hierarchical Taxonomy Visualizer to address these challenges.

Edge bundling reduces visual clutter by aggregating similar edges, yet practitioners lack a structured vocabulary for reasoning about the tasks that bundled visualizations support. Such a vocabulary is needed both to evaluate the general utility of bundling and to compare different bundling approaches. We address this gap by assembling a corpus of 102 papers, 49 of which contain explicit bundling tasks, spanning node-link diagrams, geographic trail sets, and parallel coordinate plots. From this corpus, we derive a task taxonomy organized as a matrix of scope (Element, Bundle, Global, Multi-view) crossed with action (Verify, Identify, Characterize, Quantify, Compare, Assess), instantiated across the three representation types. We show that bundling simultaneously enables tasks (bundle-level and global reasoning) and disables others (element-level precision), a duality not captured by existing task frameworks. Our coded corpus and taxonomy are released as supplemental material on OSF (osf.io/23r67).

Block-based data structures help reducing memory consumption when running algorithms on large unstructured meshes. Their functioning, however, depends on how the input mesh is subdivided into blocks. Despite this, subdivision is often treated as a fixed preprocessing step, and its impact on performance remains poorly understood. In this paper, we present a systematic study of how different subdivision strategies affect the efficiency of block-based topological data structures. We compare spatial (uniform grid, oc- tree) and topology-aware (METIS and reordered METIS) subdivisions on multiple scalar fields defined on tetrahedral meshes using three representative algorithms. Our results show that subdivision choice significantly influences performance with improvements of up to 29% in runtime. Overall, our findings highlight subdivision as a key parameter in the design and tuning of block-based data structures for large-scale topological analysis.

We introduce EventColumn, a new column type that integrates event-sequence data with heterogeneous tabular attributes into a single unified table. EventColumn lets analysts compare event sequences alongside numerical, categorical, and temporal attributes at both instance and group levels, offering a compressed overview, heatmap group summaries, alignment by event types, and boxplots of similar historical items. We developed EventColumn together with collaborators from the steel industry to facilitate the analysis of production events and warehouse logistics, but the solution applies to other event-sequence domains. Unlike most existing approaches that compare either event sequences or tables, EventColumn supports simultaneous comparison of both. We demonstrate its integration with Taggle and Microsoft Power BI on data from steel production logistics and on a public e‑commerce dataset.