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1. GraphWaGu: GPU Powered Large Scale Graph Layout Computation and Rendering for the Web.

   Landon Dyken, Pravin Poudel (June 2022, Eurographics Symposium on Parallel Graphics and Visualization)

   Bujack, Roxana and (Ed.)

   Large scale graphs are used to encode data from a variety of application domains such as social networks, the web, biological networks, road maps, and finance. Computing enriching layouts and interactive rendering play an important role in many of these applications. However, producing an efficient and interactive visualization of large graphs remains a major challenge, particularly in the web-browser. Existing state of the art web-based visualization systems such as D3.js, Stardust, and NetV.js struggle to achieve interactive layout and visualization for large scale graphs. In this work, we leverage the latest WebGPU technology to develop GraphWaGu, the first WebGPU-based graph visualization system. WebGPU is a new graphics API that brings the full capabilities of modern GPUs to the web browser. Leveraging the computational capabilities of the GPU using this technology enables GraphWaGu to scale to larger graphs than existing technologies. GraphWaGu embodies both fast parallel rendering and layout creation using modified Frutcherman-Reingold and Barnes-Hut algorithms implemented in WebGPU compute shaders. Experimental results demonstrate that our solution achieves the best performance, scalability, and layout quality when compared to current state of the art web-based graph visualization libraries. All of our source code for the project is available at https://github.com/harp-lab/GraphWaGu. 

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2. Bayesian cross-validation by parallel Markov chain Monte Carlo

   https://doi.org/10.1007/s11222-024-10404-w  

   Cooper, Alex; Vehtari, Aki; Forbes, Catherine; Simpson, Dan; Kennedy, Lauren (August 2024, Statistics and Computing)

   Brute force cross-validation (CV) is a method for predictive assessment and model selection that is general and applicable to a wide range of Bayesian models. Naive or ‘brute force’ CV approaches are often too computationally costly for interactive modeling workflows, especially when inference relies on Markov chain Monte Carlo (MCMC). We propose overcoming this limitation using massively parallel MCMC. Using accelerator hardware such as graphics processor units, our approach can be about as fast (in wall clock time) as a single full-data model fit. Parallel CV is flexible because it can easily exploit a wide range data partitioning schemes, such as those designed for non-exchangeable data. It can also accommodate a range of scoring rules. We propose MCMC diagnostics, including a summary of MCMC mixing based on the popular potential scale reduction factor (R-hat) and MCMC effective sample size (ESS) measures. We also describe a method for determining whether an R-hat diagnostic indicates approximate stationarity of the chains, that may be of more general interest for applications beyond parallel CV. Finally, we show that parallel CV and its diagnostics can be implemented with online algorithms, allowing parallel CV to scale up to very large blocking designs on memory-constrained computing accelerators. 

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3. Visual (dis)Confirmation: Validating Models and Hypotheses with Visualizations

   https://doi.org/10.1109/IV-2.2019.00032  

   Choi, In Kwon; Raveendranath, Nirmal Kumar; Westerfield, Jared; Reda, Khairi (July 2019, 23rd International Conference in Information Visualization – Part II)

   Data visualization provides a powerful way for analysts to explore and make data-driven discoveries. However, current visual analytic tools provide only limited support for hypothesis-driven inquiry, as their built-in interactions and workflows are primarily intended for exploratory analysis. Visualization tools notably lack capabilities that would allow users to visually and incrementally test the fit of their conceptual models and provisional hypotheses against the data. This imbalance could bias users to overly rely on exploratory analysis as the principal mode of inquiry, which can be detrimental to discovery. In this paper, we introduce Visual (dis) Confirmation, a tool for conducting confirmatory, hypothesis-driven analyses with visualizations. Users interact by framing hypotheses and data expectations in natural language. The system then selects conceptually relevant data features and automatically generates visualizations to validate the underlying expectations. Distinctively, the resulting visualizations also highlight places where one's mental model disagrees with the data, so as to stimulate reflection. The proposed tool represents a new class of interactive data systems capable of supporting confirmatory visual analysis, and responding more intelligently by spotlighting gaps between one's knowledge and the data. We describe the algorithmic techniques behind this workflow. We also demonstrate the utility of the tool through a case study. 

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4. Visualization Guardrails: Designing Interventions Against Cherry-Picking in Interactive Data Explorers

   https://doi.org/10.1145/3706598.3713385  

   Lisnic, Maxim; Cutler, Zach; Kogan, Marina; Lex, Alexander (April 2025, ACM)

   The growing popularity of interactive time series exploration platforms has made data visualization more accessible to the public. However, the ease of creating polished charts with preloaded data also enables selective information presentation, often resulting in biased or misleading visualizations. Research shows that these tools have been used to spread misinformation, particularly in areas such as public health and economic policies during the COVID-19 pandemic. Post hoc fact-checking may be ineffective because it typically addresses only a portion of misleading posts and comes too late to curb the spread. In this work, we explore using visualization design to counteract cherry-picking, a common tactic in deceptive visualizations. We propose a design space of guardrails—interventions to expose cherry-picking in time-series explorers. Through three crowd-sourced experiments, we demonstrate that guardrails, particularly those superimposing data, can encourage skepticism, though with some limitations. We provide recommendations for developing more effective visualization guardrails. 

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5. Interactive Visualizations for Crime Data Analysis by Mixed Reality

   Sharma, S; Dronavalli, S; Chellatore, M; Pesaladinne, R (June 2024, Virtual, Augmented and Mixed Reality. HCII 2024. Lecture Notes in Computer Science)

   Chen, JYC; Fragomeni, G eds (Ed.)

   Crime data visualization plays a key role in understanding and dealing with criminal activities. This paper focuses on the integration of mixed reality (MR) and crime data analysis. There are many barriers and challenges when developing MR three-dimensional (3D) environments for visualization and inspection. The main problem is the lack of commonly shared data structures and interfaces between them. The rise in crime rates over the past few years is a huge source of issue for police departments and law enforcement organizations. As the crime rates significantly changed throughout time, both upward and downward, these changes are then compared to external factors, such as population, unemployment, and poverty. There is a need for visualizing the multiple crime datasets in multiple states with external factors. This work proposes a novel interactive approach for loading crime datasets into the HoloLens 2 device and displaying them in a mixed-reality setting for data analysis. By allowing people to engage and analyze datasets in a 3D space, the suggested system seeks to close the gap between data analysis and machine learning. Users can import many datasets, such as spatial, category, and numerical data, into the HoloLens 2 device and interactively visualize crime data for different states simultaneously. The system offers user-friendly capabilities for interactive data visualization in mixed reality once the data has been imported. The dataset is manipulated and transformed by users, who can also rotate, scale, and position it in 3D. To depict various characteristics and dimensions of the data, the system also supports a variety of visual encoding techniques, such as color mapping, size scaling, and spatial layout with the use of the imported datasets and the HoloLens 2’s visualization capabilities, users can discover new insights and intricate linkages within the data. Natural movements and voice instructions allow users to engage with the visible data, enabling a hands-free and immersive data exploration experience. This paper also visualizes the crime data for four different cities: Chicago, Baltimore, Dallas, and Denton. Analyzing crime against factors such as population, employment, unemployment rate, and poverty rates provides information about the complex relationship between social factors and criminal behavior. The results and outcomes of this work will help the police department and law enforcement organizations better understand crime issues and supply insight into factors affecting crime that will help them deploy resources and help their decision-making process. 

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