Ashley Suh

Exploring Knowledge Graphs (KGs) remains an open challenge, as doing so requires expertise in unconventional querying languages and reasoning over complex, evolving schemas. We present LinkQ, an agentic KG-exploration system that helps users refine natural language questions into structured queries through guided large language model (LLM) collaboration. LinkQ’s design was informed by a formative study with KG experts, resulting in a human-in-the-loop interface that prioritizes transparency through visual mechanisms for verifying the agent’s behavior and generated outputs. LinkQ has five primary components: an agentic state diagram that contextualizes each stage of the agent-querying pipeline, a query editor that pairs generated queries with LLM explanations, an entity-relation ID table showing extracted KG data with semantic descriptions, a query structure graph that depicts the path traversed within the KG, and an interactive, multi-attribute graph visualization of query results. We conducted a qualitative study with 14 KG practitioners to better understand how LinkQ impacts workflows, exploration strategies, and user trust. Our findings show that the interface design enables intuitive, effective KG exploration – even for users without prior querying expertise. At the same time, we observe risks of miscalibrated trust in agentic exploration systems, emphasizing the need for future work for trust calibration in LLM-driven analytic interfaces.

Recent studies have reported a shortcoming of visual exploratory data analysis (EDA) that can lead analysts to report spurious findings. These findings have prompted the advocacy of incorporating belief elicitation within the data analysis process. However, the results from these studies primarily drew from laboratory experiments, which can differ from real-world analysis contexts. In this paper, we present outcomes from a longitudinal study with students enrolled in a university-level visual analytics course tackling the VAST Challenge. The students formed teams that were randomly assigned to the belief elicitation and non-belief elicitation conditions. Our study results indicate teams that underwent belief elicitation adopted an intentional approach, while teams in the non-belief elicitation condition reported greater diversity in findings, aligning with prior research. Surprisingly, teams from both conditions achieved equal success in solving the VAST Challenge, suggesting that analysts can incorporate belief elicitation strategically for different goals. We provide guidelines for incorporating belief elicitation into data analysis and teaching material for educators to include belief elicitation in visual analytics courses.

This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners - KG Builders, Analysts, and Consumers - each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.

Data integration is often performed to consolidate information from multiple disparate data sources during visual data analysis. However, integration operations are usually separate from visual analytics operations such as encode and filter in both interface design and empirical research. We conducted a preliminary user study to investigate whether and how data integration should be incorporated directly into the visual analytics process. We used two interface alternatives featuring contrasting approaches to the data preparation and analysis workflow: manual file-based ex-situ integration as a separate step from visual analytics operations; and automatic UI-based in-situ integration merged with visual analytics operations. Participants were asked to complete specific and free-form tasks with each interface, browsing for patterns, generating insights, and summarizing relationships between attributes distributed across multiple files. Analyzing participants’ interactions and feedback, we found both task completion time and total interactions to be similar across interfaces and tasks, as well as unique integration strategies between interfaces and emergent behaviors related to satisficing and cognitive bias. Participants’ time spent and interactions revealed that in-situ integration enabled users to spend more time on analysis tasks compared with ex-situ integration. Participants’ integration strategies and analytical behaviors revealed differences in interface usage for generating and tracking hypotheses and insights. With these results, we synthesized preliminary guidelines for designing future visual analytics interfaces that can support integrating attributes throughout an active analysis process.

Presenting a predictive model's performance is a communication bottleneck that threatens collaborations between data scientists and subject matter experts. Accuracy and error metrics alone fail to tell the whole story of a model - its risks, strengths, and limitations - making it difficult for subject matter experts to feel confident in their decision to use a model. As a result, models may fail in unexpected ways or go entirely unused, as subject matter experts disregard poorly presented models in favor of familiar, yet arguably substandard methods. In this paper, we describe an iterative study conducted with both subject matter experts and data scientists to understand the gaps in communication between these two groups. We find that, while the two groups share common goals of understanding the data and predictions of the model, friction can stem from unfamiliar terms, metrics, and visualizations - limiting the transfer of knowledge to SMEs and discouraging clarifying questions being asked during presentations. Based on our findings, we derive a set of communication guidelines that use visualization as a common medium for communicating the strengths and weaknesses of a model. We provide a demonstration of our guidelines in a regression modeling scenario and elicit feedback on their use from subject matter experts. From our demonstration, subject matter experts were more comfortable discussing a model's performance, more aware of the trade-offs for the presented model, and better equipped to assess the model's risks - ultimately informing and contextualizing the model's use beyond text and numbers.

Presenting a predictive model's performance is a communication bottleneck that threatens collaborations between data scientists and subject matter experts. Accuracy and error metrics alone fail to tell the whole story of a model – its risks, strengths, and limitations – making it difficult for subject matter experts to feel confident in their decision to use a model. As a result, models may fail in unexpected ways or go entirely unused, as subject matter experts disregard poorly presented models in favor of familiar, yet arguably substandard methods. In this paper, we describe an iterative study conducted with both subject matter experts and data scientists to understand the gaps in communication between these two groups. We find that, while the two groups share common goals of understanding the data and predictions of the model, friction can stem from unfamiliar terms, metrics, and visualizations – limiting the transfer of knowledge to SMEs and discouraging clarifying questions being asked during presentations. Based on our findings, we derive a set of communication guidelines that use visualization as a common medium for communicating the strengths and weaknesses of a model. We provide a demonstration of our guidelines in a regression modeling scenario and elicit feedback on their use from subject matter experts. From our demonstration, subject matter experts were more comfortable discussing a model's performance, more aware of the trade-offs for the presented model, and better equipped to assess the model's risks – ultimately informing and contextualizing the model's use beyond text and numbers.

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection – the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this article we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method’s utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.